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Review

A Review on the Impact of Transmission Line Compensation and RES Integration on Protection Schemes

by
Ntombenhle Mazibuko
1,*,
Kayode T. Akindeji
2 and
Katleho Moloi
2
1
Department of Electrical Power Engineering, Department of Electronics and Computer Engineering, Durban University of Technology, Durban 4000, South Africa
2
Department of Electrical Power Engineering, Smart Grid Research Centre, Durban University of Technology, Durban 4000, South Africa
*
Author to whom correspondence should be addressed.
Energies 2024, 17(14), 3433; https://doi.org/10.3390/en17143433
Submission received: 3 May 2024 / Revised: 27 June 2024 / Accepted: 2 July 2024 / Published: 12 July 2024
(This article belongs to the Section A: Sustainable Energy)

Abstract

:
South Africa is currently experiencing an energy crisis because of a mismatch between energy supply and demand. Increasing energy demand necessitates the adequate operation of generation and transmission facilities to maintain the reliability of the power system. Transmission line compensation is used to increase the ability to transfer power, thereby enhancing system stability, voltage regulation, and reactive power balance. Also, in recent years, the introduction of renewable energy sources (RES) has proven to be effective in supporting the grid by providing additional energy. As a result, the dynamics of power systems have changed, and many developing nations are adopting the integration of renewable energy into the grid to increase the aspect ratio of the energy availability factor. While both techniques contribute to the grid’s ability to meet energy demand, they frequently introduce technical challenges that affect the stability and protection of the systems. This paper provides a comprehensive review of the challenges introduced by transmission line compensation and the integration of renewable energy, as well as the various techniques proposed in the literature to address these issues. Different compensation techniques, including fault detection, classification, and location, for compensated and uncompensated transmission lines, including those connected to renewable energy sources, are reviewed. This paper then analyzes the adaptive distance protection schemes available in the literature to mitigate the impact of compensation/integration of RES into the grid. Based on the literature reviewed, it is essential for protection engineers to understand the dynamics introduced by network topology incorporating a combination of RES and heavily compensated transmission lines.

1. Introduction

Global energy cost increases, combined with the desire to minimize harmful fossil-based emissions, are driving the demand for global clean and efficient energy sources and systems [1]. As per the Department of Energy’s report in South Africa, by the end of June 2017, 3162 MW of electricity generation capacity had been added to the national grid through 57 independent power producer (IPP) projects. According to the CSRI report from February 2023, renewable energy technologies, such as wind, solar PV, and CSP, saw an increase in installed capacity in 2022, reaching a total of 6.2 GW and contributing 7.3% to the overall energy mix. The Department of Mineral Resources and Energy (DMRE) is inviting interested parties to register potential bids for the Seventh Bid Submission Phase (Bid Window 7) of the Renewable Energy Independent Power Producers Procurement Programme (REIPPPP). This call for proposals aims to solicit bids from independent power producers (IPPs) to develop 5000 MW of new generation capacity, including 1800 MW of solar PV and 3200 MW of wind power. Furthermore, the eThekwini Renewable Energy Roadmap technical report, building upon the Energy Strategic Roadmap (ESR), outlines the municipality’s climate action objectives for 2030 and 2050. Through its Municipal Independent Power Producer Programme (MIPPP), eThekwini seeks to secure 400 MW of capacity from IPPs to ensure a reliable and diverse energy supply. The MIPPP will be implemented in phases, with the aim of having this new generation capacity operational by July 2025 or sooner. These advancements highlight a clear change in the dynamics of power systems, as numerous developing countries are progressively embracing the integration of renewable energy to improve their energy accessibility. However, technical challenges arise due to this widespread adoption of renewable energy. These challenges include issues such as reduced inertia due to the replacement of traditional synchronous generators with virtual synchronous generators, fault ride-through capabilities to ensure the continuous operation of renewable energy sources (RES) during faults, uncertainties, voltage and frequency fluctuations, high fault currents, low generation reserves, and diminished power quality [2].
Compensated transmission lines are utilized within power system networks to boost energy transmission capacity, enhance system stability, reduce transmission losses, and offer greater flexibility in managing power flow [3]. The non-linear properties of a series capacitor used for line compensation cause the impedance characteristics of the system to change rapidly, resulting in the generation of high-frequency signals. These signals interfere with the proper operation of relays by affecting voltage (V) and current (I). Consequently, the relay faces challenges in accurately identifying, categorizing, and locating faults [4]. When designing a protection system for a compensated transmission line, two main occurrences must be considered: current inversion and voltage inversion resulting from compensation. If a fault arises where the total reactance is capacitive, it triggers current inversion, also known as current reversal. During this inversion, the current leads the voltage instead of lagging behind it. The probability of current inversion increases with greater levels of compensation. While current inversion is not a major issue at lower compensation levels, it becomes a concern as compensation increases. At higher levels, current inversion can affect faults along an extended section of the line, potentially reaching beyond the receiving end of the compensated line [5]. If a fault happens on a series-compensated transmission line and the impedance from the fault location to the relay point is capacitive while the overall impedance from the power system source to the fault stays inductive, it results in what’s known as a voltage inversion in the power system [6].
According to Pilotto in [7], it has been noted that compensating transmission lines can unintentionally increase the likelihood of encountering sub-synchronous resonance (SSR). This challenge arises from the interplay between an electrical mode of the series-compensated network and a mechanical shaft mode of a turbine-generator group. The repercussions of these interactions could potentially lead to the generation of harmful torsional forces. Transmission lines are formally compensated using flexible AC transmission system (FACTS) devices bringing forth novel power system dynamics that necessitate a thorough examination by system protection engineers [8]. Normally transmission lines are subjected to various fault conditions, such as L-L (line-to-line), L-G (line-to-ground), and even three-phase fault [9]. When these faults happen, the protection system is expected to isolate the faulty system; reliability, accuracy, and speed are some of the major priorities for any protection scheme. Distance protection relay is used to protect transmission line, these schemes used the system impedance seen by the relay to issue a trip signal. The effectiveness of the distance protection scheme (DPS) is significantly impacted by the integration of FACTS devices, as their inclusion may interfere with the coordination of protective relays, leading to both under-reaching and over-reaching of the relays [10]. Distance relays are commonly employed for the protection of transmission lines. Distance relays face several challenges when protecting compensated lines, such as fault detection, classification, and location, due to voltage inversion, current inversion, and low-frequency oscillation. Also, the presence of harmonics in the context of compensated transmission line protection, may cause the relay will malfunction. According to [11] in many instances, protective relays for transmission lines have unfortunately triggered due to harmonics present in the power system. The other concept that affects the distance relay when protecting compensated line is compensation level. This is described as the percentage of the capacitive reactive impedance introduced by capacitors, FACTS devices added to compensated transmission line. The capacitive reactance then cancels the inductive reactance of the line. Compensation level is dependent upon the quantity of capacitors connected to the line, hence affecting the overall impendence seen by the relay, and a proficient understanding of the compensation level is imperative for the accurate functioning of distance relays. The presence of fluctuation in the reactance of capacitors requires the implementation of adaptive distance protection mechanisms [12] to improve the performance of the relays. It is hypothesized that the complexity of a transmission line protection system may be further increased when the integration of renewable energy sources (RES) into a compensated transmission line. Consequently, the conventional transmission line protection schemes may not be sufficient, this necessitates further investigation into the impact of integrating renewable energy sources (RES) on existing protection schemes for heavily compensated lines [13]. The majority of the existing investigation of distance protection techniques whn protecting compensated lines is conducted without considering the incorporation of renewable energy sources (RES) and the associated complexities. Although some literature has assessed the impact of renewable energy sources (RES) on conventional protection schemes, most researchers have investigated for a condition where the lines at point of penetration are not compensated, and only the methodologies involving time–frequency analysis and real-time evaluation have been examined particularly for windfarms, it is also noted that most schemes are not tested or subjected to various system dynamics like power swing or higher levels of compensation.
While adaptive distance protection strategies have addressed the issue of these schemes not being subjected to testing under dynamic system conditions. The investigation of the impact of various compensation levels and high penetration of different types of renewable energy sources is lacking in the literature. This paper provides a thorough examination of the challenges posed by the integration of renewable energy sources (RES) and the significant compensation levels on transmission line protection schemes. It also explores various techniques employed to address these challenges. This paper reviews the techniques available in literature to address the challenges presented by lines compensation and RES integration into power systems network operation including the protection systems, it then makes recommendation for further research that maybe conducted to improve the protection system of compensated transmission line with penetration of RES.

2. The Landscape of Renewable Energy in South Africa

Despite recent investments in renewable energy technologies, wind, solar PV, and CSP account for a pitiful 13.7% of the total energy mix (without hydro, it is 7.3%). Coal continues to be South Africa’s primary energy source, accounting for 80% of the nation’s electricity generation. A total of 2.6% and 4.6%, respectively, came from diesel and nuclear energy [14]. In 2003, South Africa unveiled a white paper with a strategy to produce 10 TWh of power from renewable energy sources, including biomass, wind, sun, and small-scale hydro. An integrated resource plan was subsequently passed in May 2011 with the revised objective of increasing the amount of renewable energy in the energy mix to 17,800 MW by 2030 [15]. RSA’s annual power demand is predicted to increase from 345 TWh to 416 TWh by 2030, compared to the Department of Energy’s 454 TWh projection in the Integrated Resource Plan (IRP) for power document [16]. Furthermore, Eskom’s Just Energy shift (JET) office was founded in early 2020 and has advanced the shift towards a greener and cleaner energy future with notable advancements [17]. “Just” clarifies that there won’t be any detrimental effects on society, employment, or livelihoods from this transition, which is defined as the progressive shift towards lower carbon technology. The future growth and sustainability of the nation depend on the Just Energy Transition.
The installed capacity of renewable energy sources in South Africa is depicted in Figure 1, which highlights the prevalence of hydro, solar, and photovoltaic energy sources. This assertion is in fact supported by the live report reported in Table 1 on the Eskom website. The report can be accessed at the Eskom website [18]. Moreover, Table 2 presents the comprehensive annual contribution of renewable energy, derived from data provided by operators. Both Eskom and IPP contributions are included in the wind data. Table 2 displays the yearly amount of renewable energy added to Eskom’s grid in 2023–2024.
Figure 2 displays the hourly contribution. It is evident from this figure that wind is the primary source of grid enhancement, contributing up to over 2500 MWh, while photovoltaics contributes over 1750 MWh. This demonstrates how the South African grid network is increasingly integrating renewable energy sources. The integrated resource plan is a long-term energy strategy that describes the infrastructure needs and electricity generating mix for South Africa in order to provide affordable, sustainable energy security, minimal water use, and low carbon dioxide emissions [6]. The eleven geographic regions that are suitable for the large-scale development of solar PV and wind energy are referred to as the renewable energy development zones. Through optimization of the RE capacity allocation mix and future plant location in the context of national grid support, the REDZs introduce geographical dispersion in the siting of RE facilities [20], this zones are shown in Figure 3 and highlights a great potential for renewable energy integration in South Africa. Job losses in the coal industry, a lack of local manufacturing of renewable energy components, high entry barriers into the renewable energy sector due to a lack of competitiveness, a shortage of critical skills, and grid capacity for the integration of renewables in high resource areas are some of the challenges associated with renewable energy integration in South Africa. It suggests doing research and a cost–benefit analysis on alternative complementary technologies that can offer the required grid services to support variable renewable technologies in order to address the issue of the performance of renewable technologies. Therefore, in the presence of renewable intergraded networks, our research further suggests a development of improved protective systems, grid stability support and improve technologies for integrating renewables such that the grids stability is maintained.

3. FACTS Devices on Protection Schemes

FACTS devices introduce an innovative concept that utilizes power electronics and controllers to enhance the controllability and capacity expansion of transmission networks, while also offering opportunities to improve control operations and stability in transmission systems. The underlying technology of FACTS involves advanced high-power electronics that incorporate various thyristor devices for future applications. These are supported by advancements in digital protective relays, controls, and integrated communication systems. These devices enable the regulation of critical parameters and characteristics of transmission lines, such as impedance, admittance, and voltage (both angle and phase), which are fundamental constraints on power transmission. Detecting, categorizing, and locating faults in transmission lines compensated by FACTS devices and series capacitor (SC) is considerably more complex than traditional lines due to the presence of time-variant voltage and current sources in the line’s structure. Included among these devices are thyristor-controlled series capacitors (TCSC), static VAR compensators, static synchronous compensators (STATCOM), static synchronous series compensators, and unified power flow controllers (UPFC). The presence of FACTS devices and SCs in transmission lines results in continuous changes in line impedance, presenting significant challenges for line protection [23]. TCSC and UPFC emerge as the two most crucial devices, widely deployed on high-voltage transmission lines [24].
The UPFC comprises a static synchronous series compensator (STATCOM) and a static synchronous compensator (SSSC), as illustrated as Figure 6 of [25]. The parameters of voltage (V), current (I), and phase angle can be adjusted independently. This, consequently, aids in the autonomous regulation of both active and reactive power flows within a transmission line [26]. The voltage and current signals experience significant alterations as a result of the UPFC controllers, both during transient conditions and in a steady-state. Hence, the performance of the standard distance relay is significantly affected due to the nonlinearity in output power resulting from diverse operational modes of the UPFC [27]. The impedance measurement will exhibit an increase as a consequence of the incorporation of the UPCF due to the aforementioned phenomenon, it is possible for the impedance relay to exhibit over-reaching or under-reaching. In [28], UPFC was designed to regulate the voltage of transmission lines and bus voltages apart from the reactive and active electrical power they carry. It is suggested that factors such as installation point, operating modes, fault location, fault resistances, fault inception angles, fault types, and external faults must all be considered while designing the protective system of transmission line compensated using the technique.
The STATCOM acts as a controller for reactive power compensation, operating in parallel with the system. Advances in power electronics, particularly with the GTO thyristor, have made it feasible to implement this technology practically, offering a viable alternative to traditional SVCs. The torsional properties of remote generators can be modified by implementing a static VAR compensator (SVC) within an advanced transmission system that utilizes series capacitors for compensation. The torsional interaction is significantly influenced by various parameters such as system loading, the level of series compensation, the operating point and control setting of the static VAR compensator (SVC), and other relevant factors [29]. The management of reactive power flow is governed by the relationship between the AC voltage of the system and the voltage at the STATCOM’s AC terminals. If the voltage at the STATCOM terminals exceeds the system voltage, the STATCOM functions as a capacitor, injecting reactive power from the STATCOM into the system. Conversely, if the STATCOM voltage falls below the AC voltage, it behaves as an inductor, causing the reversal of reactive power flow. Under normal operating conditions, both voltages are equal, resulting in no power exchange between the STATCOM and the system [30]. The SSSC, as a series-connected FACTS device, has the capability to provide either inductive or capacitive voltage regardless of the current in the transmission line, within its rated current limits. Additionally, the SSSC has the capacity to exchange both active and reactive power with the AC system, primarily by regulating the angular position of the injected voltage [31]. In [32] The SSSC, functioning as a series-connected FACTS apparatus, is capable of supplying either inductive or capacitive voltage independently of the current in the transmission line, within its specified current limits. Moreover, the SSSC can exchange both active and reactive power with the AC system, primarily by controlling the angular position of the injected voltage [33]. According to [34] Enhancing the dynamic performance of power systems, regulating power factor, voltage management, and stabilizing power flow can be enhanced through the integration of a STATCOM. Additionally, it can effectively mitigate sub synchronous resonance (SSR). However, to adequately suppress SSR, an auxiliary controller needs to be incorporated alongside the STATCOM.
The TCSC consists of a capacitor that is connected in series with a thyristor-controlled reactor (TCR) and an antiparallel thyristor [35]. To protect the capacitor against over-voltage, a metal oxide varistor (MOV) is employed. On the contrary, the TCR alters the impedance of the TCSC by manipulating the firing angle of a thyristor, thereby boosting the fundamental voltage across the fixed capacitor. When the voltage is altered, the series capacitive reactance adapts correspondingly [9]. Hence, the impedance seen by the relay is influenced by the presence of a TCSC in the fault loop. These influences affect both the inductive and capacitive modes of operation [36]. When thyristors are triggered in close proximity to the zero crossing of the capacitor voltage, the capacitive reactance has the potential to increase up to 2–3 times the fixed capacitor reactance. Miscoordination, over-reaching, and other operational challenges arise when the TCSC switches between different modes of operation. To mitigate these challenges, it is recommended to employ adaptive and pilot protection schemes. Since the positive-sequence impedance as determined by the conventional stand-alone distance relays becomes ineffective in determining the fault distance due to rapid changes brought about by the associated TCSC control actions in the primary system parameters, including line impedances and load currents, throughout the fault duration [37].
Power swings are also a consequential matter arising from line compensation, as the impedance seen by the distance relay during a power swing may encroach upon the relay’s operational region, which is deemed undesirable [38], this occurrence will result in an erroneous trip and instigate instability within a system. The occurrence of an out-of-step issue may arise when the swing exhibits instability, leading to a loss of synchronization within the system [39]. The implementation of an out-of-step blocking (OSB) strategy prevents the inhibition of protection relays’ activation in the presence of a stable power swing [38,40]. The presence of harmonics introduced by implementing transmission line compensation on the power system networks affects the accuracy of the distance protection; the first-zone protection would not detect the faults at the reach setting [11].
In [41], smart power flow control (SPFC) is introduced, utilizing a 48-pulse converter, compensates for the reactive power of the power grid. The maximum power is achieved in SSSC at an injected voltage angle of 90 degrees when using a 48-pulse STATCOM, and harmonic distortion is minimized. In [42,43] a VSC-Base STATCOM is investigated using ANN-based closed-loop control techniques, this article shows how to regulate the reactive power produced by a long transmission line across a broad area, keeping the voltage profile at the receiving end constant. Harmonics are reduced to a tolerable level using the ANN method and H-bridge multi-level VSC architecture. Authors in [44] used a fuzzy logic (FL)-based controller for UPFC, in their work lines current total harmonic distortion (THD) was reduced below 5%, reactive power adjustment, and voltage sag mitigation. Furthermore, a comparison is made between the performance of the proposed FL-based UPFC and proportional integral (PI)-based controller under varying transmission network operating conditions, with the results showing that the FL-based UPFC provides superior results.
Depending on the design, these compensation devices may be situated in various locations, such as the midpoint of the line, at the busbar near the generating station, or at the distant busbar toward the load. Determining the optimal placement of these devices is crucial for maximizing their benefits. Achieving desired operating characteristics involves continuously adjusting the location of the FACTS device along the transmission line [45,46]. According to [47] FACTS devices achieve optimal voltage support when strategically positioned at the midpoint of the transmission line. Moreover, an in-depth examination of FACTS device applications is presented in article [46], which reviews research from the past decade on different methodologies for placing FACTS devices. This research utilizes a meta-heuristic approach to address the placement of FACTS devices, aiming to maintain appropriate bus voltages, control line flow, and enhance overall system efficiency. The literature provides an extensive analysis of the prospective methodologies aimed at mitigating the emerging challenges associated with introduction of FACTS devices for transmission line compensation; this is summarized in Table 3, highlighting different techniques and their contribution, including the challenges each work is trying to address.
Static VAR compensator (SVC) is a first-generation FACTS device that can control voltage at the required bus thereby improving the voltage profile of the system. The primary task of an SVC is to maintain the voltage at a particular bus by means of reactive power compensation (obtained by varying the firing angle of the thyristors) [9]. SVCs have been used for high-performance steady-state and transient voltage control compared with classical shunt compensation. SVCs are also used to dampen power swings, improve transient stability, and reduce system losses by optimized reactive power control [51]. Thyristor-controlled series capacitor (TCSC) is one of the important members of FACTS family that is increasingly applied with long transmission lines by the utilities in modern power systems. It can have various roles in the operation and control of power systems, such as scheduling power flow; decreasing unsymmetrical components; reducing net loss; providing voltage support; limiting short-circuit currents; mitigating sub synchronous resonance (SSR); damping the power oscillation; and enhancing transient stability [52]. Among the available FACTS devices, the unified power flow controller (UPFC) is the most versatile one that can be used to improve steady-state stability, dynamic stability and transient stability [53]. The UPFC can independently control many parameters since it is the combination of static synchronous compensator (STATCOM) and SSSC. It has been reported in many papers that UPFC can improve stability of single machine infinite bus (SMIB) system and multimachine system [54,55]. Transmission line compensation is subdivided into three categories, which are shunt series compensation and combination of series parallel; this is shown in Figure 4. Furthermore, the technology used in each category is listed for each classification

4. Renewable Energy Integration

While the integration of high-level renewable energy sources (RES) mitigates adverse environmental effects in contrast to traditional fossil fuel-based energy generation, it also presents technical challenges. These challenges include decreased total inertia, limited fault ride-through capability, increased uncertainties, fluctuations in voltage and frequency, elevated fault currents, diminished generation reserves, and reduced power quality. [2]. Another challenge with integration of RES into the grid is the significant issues is the drop in short circuit levels (SCLs), which can result in poor power quality and the failure of some protection schemes, such as distance and over-current protection [56]. Solar PV plants have zero inertia to contribute to the power grid and variable speed wind turbines have a negligible amount, it is crucial to develop suitable controlling mechanism for RES that mimic the characteristics of a synchronous generator to enhance the grid’s frequency response. The frequency of RES integrated systems is supported by the notion of virtual inertia technologies, which make use of energy storage systems (ESSs), PE converters, and control algorithms. Grid-connected solar PV always works at its maximum power point (MPP) with no reserve margin, it cannot contribute to frequency management during negative frequency excursion. To address these challenges these methods are discussed in the literature, which are energy storage devices [57], ref. [58] de-loading [59,60,61,62], and inertial response [63]. Security considerations arise regarding the practicality of integrating large-scale wind RES into the current electrical grid, particularly concerning transmission infrastructure [34]. Frequency responsiveness of wind integrated systems can be enhanced by the application of virtual inertia technologies such de-loading, inertia emulation, droop controller, and energy storage.
Also, the prompt response to disturbances by disconnecting photovoltaic (PV) and wind plants can have adverse effects on the stability of the system. Hence, it is imperative for photovoltaic (PV) and wind power plants to maintain grid connectivity in the event of faults for a specified duration, ensuring their fault ride-through (FRT) capabilities. The stipulation is primarily attributed to the contemporary grid code, which may vary across countries based on various criteria. The requirements specified by the South African grid code include voltage limits of ±1 per unit (pu), frequency limits of ±5%, and limits on current/voltage harmonic distortions, specifically a total harmonic distortion voltage (THDv) of 0.1% and a total harmonic distortion current (THDi) of 5% [64]. For the purpose of enhancing fault ride-through (FRT), a concise summary of various methodologies, as presented in the literature, is categorized in accordance with Figure 5. They are classified as per their configuration that requires auxiliary devices and those that do not require these devices [64].
Power electronic converters play a crucial role as the central processing units of a renewable energy system and grid integrated systems. The presence of these components induces harmonic injection, leading to system destabilization in the entire system. The enhancement of power quality in renewable energy systems (RES) is achieved through the implementation of diverse techniques, such as the integration of advanced control systems and the utilization of various ancillary equipment [65]. These can be classified based into four categories which are use of Facts devices, using energy storage technologies, the use of filtering technologies and different design for converter control as shown in Figure 6. Furthermore, each category uses specific technologies to achieved an improved power quality for example under battery storage system there is use of batteries, supercharged capacitors etc.
It is evident that integrating RES affects the operation and stability of the power systems network. Frequency fluctuations is one of the challenges these integrated systems encounter. The incorporation of frequency support is increasingly crucial in evolving standards for grid-connected renewable energy sources to maintain the safety and reliability of these power systems with low inertia. Given this prevailing situation, it becomes essential to integrate inverter-based renewable energy generators to improve the frequency stability of modern electrical grid [66]. The primary frequency response (PFR) and inertia work together synergistically to actively mitigate power frequency fluctuations, thus preventing potential negative outcomes like the activation of under-frequency load-shedding (UFLS) relays, false tripping of protection relays, damage to machinery, or the occurrence of unstable frequencies that could result in a blackout [67]. Renewable energy storage systems (RESS) maintain a greater power reserve to offset the reduced levels of power system inertia. To bolster the grid’s resilience against frequency disturbances, an accelerated frequency-Watt response can be integrated [68]. Improvements in frequency response for PV inverters can be deemed impractical [69], hence the power reserve is an essential requirement for providing complete support to the frequency [70].
The task of ensuring frequency stability in advanced grids is progressively growing in complexity, necessitating the development of grid codes and standards in numerous countries as a means to address this challenge. As the adoption of renewable energy sources (RES) increases, the number of primary and secondary control reserve power generating units will decrease. This results in a rise in frequency deviation, as indicated in [16]. The integration of the energy storage system (ESS) into the power grid is aimed at facilitating frequency support. Nevertheless, the implementation of this particular approach for delivering frequency support is deemed expensive [71]. The construction of the energy storage system (ESS) can be accomplished through the utilization of diverse energy storage devices, such as batteries [72,73,74], electric double-layer capacitor [59], and storage buffer units [75]. However the incorporation of virtual synchronous generators (VSG} technology into commercial photovoltaic (PV) power plants is impacted by the prevalence of energy storage systems (ESSs), one of the disadvantage on using batteries for energy reserve is reduced life span due to when a battery is required to sustain a primary frequency, it is subjected to frequent and substantial power fluctuations [76]. Marzebali et.al [77] developed a hybrid energy storage system that incorporates a fuel cell as the primary power source, supplemented by a battery for additional power supply. In [78], in order to mitigate voltage and frequency fluctuations within the isolated micro grids, it is suggested that battery energy storage systems (BESs) incorporate a synchronized and integrated energy management system, known as coordinated energy management system (SAMGs). The power outputs of renewable sources exhibit significant variability, whereas battery power densities are comparatively low. Following significant voltage fluctuations, batteries encounter difficulties in initiating start-up subsequent to rapid oscillation. A higher magnitude of reserve power is maintained for photovoltaic (PV) systems when operating at reduced levels of power system inertia.
Overloading, voltage fluctuations, and insufficient frequency support capacity are among the potential issues that may arise from continuous maximum power point tracking (MPPT) operation [79], It is advisable to consider the possibility of implementing a faster frequency-Watt response to enhance grid support during frequency disturbances. In [70] a power reserve control (PRC) technique based on maximum power point tracking (MPPT) has been established. This technique enables real-time measurement of the maximum power point (MAP) at regular intervals, eliminating the requirement for supplementary hardware or intricate computations. The coupling of the MAP measuring loop with the power reserve loop poses a hindrance to the implementation of the virtual inertia control. In order to optimize the frequency response in power networks that have a substantial integration of renewable energy resources, it is advisable to implement frequency droop-based control [80]. The summary of the techniques implemented for frequency support for grid integrated networks is shown in Table 4.
The methods employed in the literature to address the issue of FRT capabilities have been succinctly summarized in Table 5.
Furthermore Figure 4 shows a summarized model of strategies that can be used to improve frequency response of RES integrated networks. They are categorized into three, which are mathematically based, equipment-based, and control-based, as shown in Figure 7. Also, under each category the list of specific technique falling under each category for example under mathematically based there is root cause analysis and frequency-based calculations.
In the event of a power system fault resulting in a voltage drop, it is imperative that the integrated renewable energy sources (RES) exhibit the capability to sustain uninterrupted operation while remaining connected to the grid for an extended duration. Furthermore, these RES should promptly contribute to the power grid’s swift restoration following a fault-induced disconnection. The primary objective behind these measures is to guarantee the ongoing safe and stable functioning of the grid. Consequently, it is imperative that wind generators possess specific low voltage ride-through (LVRT) capability. To ensure a secure and efficient integration into the power system, it is necessary that grid-feeding inverters possess significantly improved monitoring capabilities for operation and enhanced performance. These specific inverters are required to enhance grid stability during fault conditions while maintaining continuous connection to the system. When the primary grid is experiencing an imbalance, addressing this situation becomes challenging. According to [90] has been identified that there are two primary challenges encountered during a fault condition in RES integrated networks which are systems frequency response and fault ride-through capabilities.
The investigation of wind energy utilization has been the subject of numerous investigations by researchers [66], and recently, there has been a significant an increase in the increase of wind farms having a capacity of one megawatt or higher, to attain fault ride-through capability (FRTC) even under zero system voltage conditions, various techniques and their corresponding control systems are thoroughly examined during both symmetrical and asymmetrical breakdown scenarios.
Development and integration of renewable energy sources into the existing power system has enhanced the complexity in the network. Efficient operation of this complex network is a tedious task for the authorities. It is emphasized that accurate interpretation of these codes holds paramount importance for wind farm developers, manufacturers, and network operators. These codes delineate the operational limits of RES connected to the grid, encompassing parameters such as frequency range, voltage tolerance, power factor, and fault ride-through capability. The emergence of new power-electronic technology assumes a crucial role in facilitating the integration of renewable energy sources into the grid. This technology should enable the development of power-electronic interfaces tailored to accommodate the highest projected turbine ratings. This entails optimizing energy conversion and transmission, managing reactive power, minimizing harmonic distortion, achieving high efficiency across a broad power spectrum, ensuring reliability, and withstanding subsystem component failures. Additionally, it is imperative to comprehend the impact of these device characteristics on protection systems.

5. The Impact of Renewable Energy on Protection Schemes

Designing relay tripping characteristics presents a significant challenge for distance relays on transmission lines, particularly given the increased pressure on power system operation. The increasing presence of non-synchronous and inverter-connected generating plants, driven by the rise of renewable energy sources (RES) in electricity generation, poses unique challenges. Unlike conventional plants, RES plants utilize active control systems or sophisticated software to manage disturbances. Protecting grid-integrated RES presents several issues, including reduced fault current leading to protection blindness, false or sympathetic tripping due to bidirectional fault current flow, and coordination problems where relays may trip prematurely. The distance relay may face challenges of both under-reach and over-reach, due to the factors mentioned. Additionally, islanding problems may arise, causing unstable operation, while loss of coordination can disrupt relay operation in a cascade manner. Auto-recloser challenges may result in the conversion of temporary faults into permanent ones, and selectivity issues can make it difficult for relays to distinguish between healthy and unhealthy system conditions. Distance relay challenges further complicate the task of accurately detecting faults within the network. These complexities highlight the importance of implementing effective protection systems for grid-integrated RES.
Changes in wind conditions notably affect the reach of distance relays designed for transmission line protection. Wind speed fluctuations cause voltage level changes at local network buses, causing variations in apparent impedance detected by protective relays. These fluctuations in impedance also result in adjustments to the distance relay’s reach setting [96]. The implementation of new grid regulations regarding fault ride-through (FRT) capability, requiring doubly fed induction generation (DFIG) to remain connected to the grid during fault conditions, may result in converter damage due to current fluctuations. In order to safeguard the converter, crowbar protection is employed to divert current away from it. As a result of the crowbar resistance, DFIG exhibits varying fault current values compared to normal operating conditions for specific durations [97].
This leads to issues with the reach of transmission line protection connecting these RES to the grid. The short-circuit behavior of various distributed generators (DGs), including induction generators and conventional synchronous generators, differs and influences the settings for distance protection. Moreover, the increased presence of renewable energy systems (RES) may disrupt the operation of transmission line relays. The effectiveness of transmission-line distance protection can be influenced by factors such as the capacity, size, and density of RES plants. Other factors impacting the performance of distance protection include the proximity of the fault to the bus, fault location, the type of fault (transient or steady-state), fault inception angle, power swing, voltage level, fault level, frequency matching, mutual coupling, compensation techniques, and the use of FACTS devices in transmission lines to optimize power transfer [98]. These difficulties underscore the importance of exploring novel technologies for safeguarding transmission lines. Researchers in the literature have explored various parameters influenced by the characteristics of integrated networks. Furthermore, the inclusion of FACTS devices in the transmission system significantly influences the performance of distance relays by changing apparent impedance. Additionally, the reach setting of the relay is significantly affected by ongoing fluctuations in relay end voltage when offshore wind farms are connected to the power transmission system. Consequently, developing tripping characteristics for suitable operating conditions remains a challenging issue [99].
Figure 8 shows the number of factors affecting the performance of the distance relay when protecting RES. In this figure, there are two major contributing factor which are the type of RES, penetration level, voltage level which affects parameters such as impedance. Furthermore, it can be noted that these parameters affect fault condition, systems frequency response, etc., which directly affects the effectiveness of the protection relay. The influence of source impedance is an essential factor to take into account as it is closely linked to the number of wind farms integrated into the transmission network [99]. This is because the equivalent source impedance of the generators will change depending on the number of units connected to the bus simultaneously [100]. The traditional distance protection philosophy, tailored for lines connected to synchronous generators (SG), functions effectively due to the homogeneity of SG-connected systems. This is not the case for RES integrated systems, where fault current and its angle are governed by control strategies, grid codes, and FRT requirements. Additionally, the distinctive fault current behavior of inverter-based generators (IBG) significantly affects the reliability and security of distance relays zones of protection [101] for the faults falling within the three zones. According to [102], due to the current source representation of the RES integration [2], the phase difference between line-end currents during Zone 1 faults can be considerable and depends on grid code (GC) specifications. This can lead to inaccurate reactance calculations by the distance relay of such systems, potentially compromising relay reliability. In the case of Zone 2 faults, discrepancies in both phase and magnitude between local and infeed currents could jeopardize relay security and render FRT schemes ineffective within the integrated RES. Alternatively, it might bypass backup protection, depending on the system’s characteristics.
Fast determination of the transient frequency leads to significantly decreased delay time in protection compared to using a fixed-delay method. Theoretical analysis suggests that both the power frequency component distance relay and the phase-comparison distance relay are influenced by the prominent harmonic content and frequency deviation of RES systems. Moreover, the fluctuation in power frequency impacts the distance relay because of the impedance properties of the line connected to renewable RES [103] is directly affected by frequency variation. The research that has been currently conducted is summarized in Table 6, highlighting the problem addressed and the proposed solution.
Additionally, reactive power compensation objectives are achieved by integrating a distributed static synchronous compensator (D-STATCOM) at the coupling point. This indicates that there is a need for protection engineers and researchers to focus specifically on enhancing the performance of distance protection relays in the presence of RES.

6. Adaptive Distance Protection Scheme

Integration of renewable energy affects the performance of conventional protection schemes. According to [107] the conventional relay protection employed for the collector line of a wind farm encounters issues pertaining to inadequate selectivity and diminished sensitivity. This arises due to the utilization of a solitary protection component on the bus-side of the collector line. The fluctuations in wind speed directly impact the variations in voltage, frequency, and power generation of wind farms. Hence, it is of utmost importance to implement an adaptive system for distance protection of the transmission lines that interconnect said farm with the power grid is essential [108]. The effectiveness of a distance relay may be notably influenced by several uncontrolled variables, including fault resistance, fault type, fault location, and noise. Furthermore, the integration of photovoltaic power plants into electrical grids results in unique fault current characteristics compared to conventional power systems with synchronous generators [109].
Firstly, the short-circuit level could either decrease or increase. Secondly, the fault ride-through (FRT) capability may decline. All of these factors could lead to inferior power quality and the malfunction of certain protection schemes, such as distance and over-current protection [12]. As the primary aim of a protection system is swift fault elimination, while power quality ensures the consistent delivery of reliable power within defined parameters, it is crucial to explore how protection systems are impacted by the integration of renewable energy sources (RES). The challenge stemming from RES integration lies in the fluctuating operating conditions of wind farms (WF), which can result in power, frequency, and voltage fluctuations, potentially introducing new hurdles for existing protection algorithms [110]. The incorporation of renewable energy sources into power networks alters the network topologies. Their fault levels are intermittent, and existing protection schemes may fail to operate due to their predefined conditions. Therefore, it is crucial to design and select an appropriate protection scheme for reliable control and operation of renewable integrated power systems, as conventional line protection schemes rely on preset settings and are not well-suited for dynamic operating conditions. Implementing an adaptive protection scheme may be a viable solution for protecting RES-integrated systems. According to the literature, challenges in protecting RES-integrated networks include protection blindness, false or sympathetic tripping, islanding issues, and loss of coordination [96].
According to [111], it has been noted that traditional distance relay features are highly susceptible to malfunctions triggered by both under-reaching and over-reaching the fault point when a transmission line is compensated. As a result, employing these relay features in such situations is considered unsuitable. The influence of positioning the static VAR compensator (SVC) in the middle on the ideal impedance characteristic of a distance relay is simulated in [112]. Simulation outcomes of the adaptive distance protection for the single-source power system are examined when a phase A-to-ground fault arises through various transition resistances located 70 km away from the relay. The fault resistance is simulated at 0, 10, 50, and 100 ohms. It is concluded that the proposed scheme remains unaffected by the transition resistance and transmission line compensation. According to [113], with the static VAR compensator (SVC) introduced in the fault loop, the traditional distance relay features are significantly prone to operational errors, including both under-reaching and over-reaching the fault point. Hence, the conventional characteristics cannot be effectively utilized when the static SVC is present. The impact of the SVC on the ideal impedance profile of the distance relay is analyzed in cases where the SVC is positioned at the midpoint and the far end is studied in [114]. The findings indicate that when the SVC is not within the fault loop and the fault resistance is zero, there is a minimal impact on the ideal tripping characteristics of the distance relay. However, when the SVC is within the fault loop and the fault resistance is zero, there is a notable alteration in the tripping characteristics. This is particularly evident when the impedance of the SVC shifts from inductive to capacitive. In [115], Kalman and adaptive Kalman filters and the average classification time of the adaptive model is 1.3 ms. In [116], decision tree-based classifier created in real time, providing out-of-phase safety during power swing. ANFIS and SVM are also used to clear a three-phase-to-ground fault with a fault resistance of one in 0.25 s. The impacts of phase shifting transformer (PST) is investigated [117], using analytical and computational methods, for the first time the impacts of PST on the distance relays are investigated. Results reveal that the PST causes the distance relays to under-reach. The author in [118] used machine learning model to develop overcurrent and distance protection scheme for transmission line, a hybrid artificial neural network and support vector machine (ANN-SVM) model is proposed for state recognition in microgrids, which utilizes the growing massive data streams in smart grids. The protection scheme based on impedance complex plane is developed in [119], the proposed scheme is fault resistance-immune and very flexible, making it suitable for use in a wide variety of system configurations. In [120], an adaptive scheme that using local data to determine the phase angle associated with the current is developed distance protection method using local data is proposed for transmission lines connecting renewable plants. The proposed method calculates the phase angle of faulted loop current by determining the pure-fault impedance of the renewable plant at every instant following fault detection, irrespective of the control scheme associated with the plant. Utilizing the information, it calculates the line impedance up to the fault point accurately. An artificial neural network (ANN)-based adaptive protection scheme was developed in [121], the proposed strategy uses data from locally linked field instruments to calculate fault resistance, which is then used to automatically alter the relay settings. An adaptive protection scheme employing a radial basis function neural network (RBFNN) is discussed in [122], which utilizes infeed current, voltage, and impedance data measured at the remote ends of the transmission line by the remote terminal units, this scheme automatically adjusts its relay operational settings based on acquisition of remote end infeed data from the remote substation. In [123], both the original heap-based optimization (HBO) and a modified version (MHBO) of the algorithms were employed. These algorithms successfully addressed challenges in coordinating distance protection relays, and their performance was evaluated on a 400 MW grid-connected microgrid.
In [124], a scheme is suggested that relies on the modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) technique. This algorithm serves as a supervisory scheme for determining the backup zone setting (Z3) of the distance relay. Pilot-superimposed impedance (PSI) is proposed in this scheme where there is no requirement to send the voltage readings from the distant bus [125]; bandwidth is saved while simultaneously increasing the data’s dependability. Demonstration of the effect infeed has on the existing distance protection approach is discussed in [126], the authors also proposed a novel algorithm for distance relays that can adaptively modify for the influence of infeed by measuring phasors in synchronization. The model that analyzes the hybrid system’s response to asymmetrical faults involves real-time calculations of the equivalent impedance of the power grid and the current flowing through the fault point is discussed in [127]. The authors in [128] build a prototype using an MCU (LPC2368_ARM7TDMI) for experimental purposes. This study also explores the impact of mutual coupling. Additionally, a Mho distance relay utilizing a phase comparator scheme is developed in [129,130]. The root-mean-square (RMS) of the positive sequence current within the faulty loop is contrasted with the TCSC terminal current. A phase comparator is suggested for the ground and phase distance elements, utilizing the positive sequence voltage as a polarized memory component. In the event of a fault occurring on the opposite side of the relay, the adaptive quadrilateral setting will transition to the third quadrant of the RX plane in coordination with the other relays safeguarding the additional feeds from the same bus. The study [131] examines the adaptability of distance protection in response to fault conditions in an offshore wind farm connected via VSC-HVDC. It utilizes the power frequency component (DPPFC) of the transmission line. In [132], dynamic frequency estimation-based adaptive protection scheme has been devised, employing a multiple signal classification approach to gauge the dynamic frequency. When the estimated frequency closely aligns with the fundamental frequency, the distance requirement is activated. Consequently, the regular distance protection remains unaffected by the frequency variation of the fault current. An adaptive distance protection scheme is developed in [133]; the proposed method revolves around analyzing the influence of integration of largescale The proposed approach focuses on examining how large-scale RES affect the changes in observed transmission line impedance, enabling accurate fault identification and localization. Additionally, the methodology factors in parameters such as fault location, fault resistance, fault type, fluctuations in LSPPP generation, and varying noise conditions when determining the phase angle of the fault loop current.

7. Fault Detection, Classification, and Location

Distance protection schemes typically comprise three sub-units, specifically fault detection, classification, and location [27]. The efficacy of the distance protection technique is contingent upon the speed and precision of the initial two units. Power grid is expected to affect the effectiveness of traditional protection relay systems, which were originally tailored for setups dominated by synchronous generators (SGs). Consequently, the suggested approach focuses on assessing how RES influence changes in the observed impedance of transmission lines to enhance the accurate identification and localization of faults [133]. The following mathematical application of techniques are popular in the context of fault detection, classification, and location, as further classified in Table 7:
  • Fourier transform (FT): used for signal analysis in the frequency domain.
  • Wavelet transform (WT): defect diagnosis systems as a feature extraction method. The literature shows that decomposing the original current and voltage signals with discrete WT (DWT) rather than continuous WT (CWT) reveals properties of the signals across numerous frequency bands [134].
  • S-transform (ST): provides joint time–frequency representation with frequency-dependent resolution based on a moving and scalable localizing Gaussian window. The two-dimensional time–frequency representation of ST can effectively reveal local spectral characteristics that are especially useful in detecting and interpreting transient events [135].
The process of fault classification presents several challenges due to various factors. The determination of both the fault type, such as line-to-ground or line-to-line, and the estimation of fault direction are of utmost importance. The task of fault localization and classification in a series compensated line is a crucial and challenging matter. Transmission line protection utilizes fault detection and pattern identification methodologies, employing intelligent techniques such as symbolic expert systems, neural networks, and fuzzy logic systems. The input signals for the classifier design under consideration are derived from the measurements of the three-phase voltage and current in the power system as illustrated in Figure 6, which illustrates the flowchart for the complete protection scheme. Firstly, feature extraction is performed to reduce signal processing without losing the original data. The extracted feature is further processed to detect, classify, and locate faults. The challenges associated with classification and fault localization exhibit comparable complexities. Within advanced signal processing methodologies, wavelet transform (WT) stands out as a widely utilized tool extensively employed in fault detection research. This method involves decomposing the signal into high- and low-frequency bands, referred to as approximate and detailed coefficients [34]. In [151], the authors introduced a novel fault location method for transmission lines, which combines wavelet packet decomposition (WPD) with support vector regression (SVR). This approach caters to different fault types, locations, fault resistances, and fault inception angles within a series compensated scheme. It utilizes regression to associate WPD sub-band energies of various fault types with fault locations on the modeled transmission line. Furthermore, it is observed that the inclusion of a low-pass filter improves the accuracy of the method. Figure 9 shows the process flow when applying fault detection, classification and location algorithm for protection relays. Voltage and current signals are collected using different methods at the relay bus during fault conditions. Afterward, feature extraction is performed; feature extraction is a method for reducing the dimensions of data. It produces a more compact and informative set of attributes. The number of features can be specified by the user or determined by the algorithm. Fault detection and diagnosis typically involves classifying faults after extracting features, a process that includes selecting the classification algorithm, while location is the information given as a response of the protection scheme in regards of the fault location.
Because of the variations in fault characteristics and the unpredictable nature of RES, the data observed may not be suitable for the wide area protection system (WITS) during faults. Therefore, advanced signal processing techniques are employed to enhance the effectiveness of protection algorithms. Additionally, the presence of series compensation alters the apparent impedance, significantly affecting the performance of distance relays. These methods include using microphone arrays and thermal imaging cameras [153]; the adaptive cumulative sum method (ACUSUM) [154]; a sequence-based method using positive sequence component of voltage (V) and (I) [155]; a fuzzy-logic-based algorithm [156]; a correlation factor-based [135] for combined fault detection and classification [140]; data-mining modeling; traveling wave-based protection scheme using game theory [128]; techniques that also include fault classification and location that are traveling waves applying fast discrete S-transform (FDST) [157]; a real-time analysis of a time-frequency-based technique [158]; a current-based Wigner distribution index (WD-index) and a voltage-based alienation index (ALN-index) [159], based on pre- and post-fault positive sequence components of V and I [160]; using high-frequency signals generated by V drops at the fault point, which eliminates the impact of frequency offset and the weak feed of RES on distance protection [161]; an adaptive property for removing the detrimental effects of fault resistance and static synchronous compensator (STATCOM) on distance protection scheme [162]; an empirical wavelet transform (EWT), Hilbert transform (HT), and weighted random vector functional link network (WRVFLN) [163,164], based on a convolutional sparse auto encoder [165] using phasor measurement units (PMUs) [166], supervised learning-based intelligent schemes, like artificial neural network (ANN), support vector machines (SVM), and a decision tree (DT); a k-nearest neighbors (k-NN) neighborhood component analysis (NCA); a minimum redundancy maximum relevance (mRMR); sequential feature selection (SFS) [167,168,169,170,171,172]; cooperative game theory; traveling waves; and a discrete wavelet transform (DWT) [173]. Refs. [174,175] Cooperative game theory, traveling waves and Discrete Wavelet Transform (DWT). In [176], an alternative approach known as the reverse synchronous reference frame (RSRF) technique is proposed for rapid fault detection and classification. Subsequently, an analytical algorithm based on symmetrical components theory is introduced to accurately determine fault location. Three potential configurations of the series capacitor bank (SCB) placement on the line are assessed. Comprehensive modeling of the SCB and its associated protective metal oxide surge arrestor (MOV) is conducted to understand its behavior during a fault. Research in signal processing has been extensively explored in the existing literature, especially in the development of tools for detection purposes. A common challenge faced by many time–frequency domains tools is their reliance on high sampling frequencies, rendering them impractical for real-time applications. Additionally, current methods often overlook crucial factors contributing to the failure of detection schemes and commonly these techniques are applied to improve the performance of a distance relay when protecting compensated transmission lines most did not consider the impact of various compensation levels and penetration level is not significantly studies.

8. Conclusions

The introduction of flexible AC transmission system (FACTS) devices presents several challenges across multiple dimensions. These include the introduction of harmonics into the system, which can disrupt fault loop currents, as well as the torsional interaction and nonlinear and fluctuating characteristics inherent in these devices. Factors such as the Ferranti effect, the effects of switching operations and firing angles, and the potential rise in reactance observed by relays near zero capacitor crossings also need to be considered. Proper placement of these devices along the line is critical, as it impacts both transient and steady-state voltage and current signals, thereby affecting the performance of distance protection relays in fault detection, classification, and localization, due to modifications in the apparent impedance seen by the relay.
Various factors contribute to the malfunction of a distance protection relay, including over-reaching and under-reaching. To enhance the performance of distance protection relays in terms of accuracy and signal processing speed, schemes based on computational intelligence and diverse methodologies are recommended. These include control methods based on artificial neural networks (ANNs), fuzzy logic controllers, and PI controllers. Additionally, machine learning and data mining techniques, such as decision trees, Kalman filters, and adaptive Kalman filters, along with various associated methods, are being explored. Furthermore, to effectively address technical challenges such as the impact of remote in feeds, out-of-step tripping during power swings, high-resistance faults, relay accuracy and speed, and the consequences of changing transformers and transmission lines, adaptive protection techniques are being employed. However, it is noteworthy that these techniques have yet to undergo testing in the presence of renewable energy sources (RES) on compensated transmission lines, and there is a lack of investigation into the impact of various compensation levels in the literature.
Moreover, limited information is available on the methodologies involving time–frequency analysis and real-time evaluation at the point of integration where the line is compensated. The recognized impact of RES integration on the distribution network primarily focuses on enhancing frequency response and fault ride-through capabilities, as well as characterizing low inertia in RES, without delving into the analysis of these phenomena in distance protection schemes. Researchers have explored the implementation of adaptive distance protection for transmission lines connected to wind farms, particularly those without compensated transmission lines. Although these methodologies have undergone extensive testing under various system operating conditions, they have yet to establish a solid foundation within the literature concerning the integration of RES into compensated transmission lines.

9. Recommendations

Existing is a compelling opportunity for further research into the implications of integrating renewable energy sources (RES) into compensated transmission lines, particularly concerning the distance protection scheme. Future investigations should aim to develop an adaptive protection system capable of accurately identifying, classifying, and locating faults using time–frequency analysis and machine learning techniques. Rigorous testing of the network topology under various system dynamics is essential to ensure both speed and accuracy. Additionally, the utilization of IoT technology presents several challenges, including real-time structural awareness, rapid and precise fault localization, fault detection, fault classification, cost reduction, and condition-based maintenance. These challenges can be addressed through the implementation of a wireless sensor network IoT for monitoring and managing transmission and distribution lines. Advancements in IoT offer improved methods for overcoming protection challenges and implementing smart grid policies. Further research can explore the impact of RES on protection systems, particularly in scenarios where underground cables are utilized at the point of connection, considering their unique characteristics. Various aspects such as fault detection and localization, fault clearance, ground fault protection, cable thermal protection, communication and coordination, maintenance and testing, and cybersecurity can be investigated within the context of digital protection schemes. Moreover, the integration of renewable energy networks into high-voltage direct current (HVDC) systems requires a comprehensive approach encompassing cybersecurity measures, physical security measures, resilient monitoring, and response systems. Establishing an effective collaboration framework among government agencies, energy providers, technology vendors, and cybersecurity experts is crucial for ensuring the protection and resilience of renewable energy and HVDC network systems. Researchers can explore ways to enhance the protection philosophy of DC transmission networks.
Ensuring the reliable and safe operation of hybrid systems, which involves the integration of various energy sources and technologies, is paramount. Robust cybersecurity measures are essential for protecting hybrid systems, given their reliance on digital controls and communication networks. Continuous monitoring, meticulous maintenance, and rigorous testing are imperative to guarantee the sustained dependability and safety of these hybrid systems. The implementation of the DC optimal power flow (DC-OPF) approach may introduce capacity problems that trigger protective systems, data privacy, and cybersecurity issues. Therefore, studying the effects of DC-OPF implementation on protection systems is worthy of investigation. Furthermore, the transmission network expansion planning (TNEP) model offers significant benefits in terms of cost optimization and anticipating future energy requirements. However, it also presents numerous challenges related to complexity, data accuracy, compliance, initial expenditures, and the need for continuous adjustment to evolving conditions. The protection system for the Transmission Network Expansion Project (TNEP) plays a crucial role in maintaining the stability and security of the transmission network. Hence, researchers should thoroughly examine the protection philosophy, considering the DC TNEP.

Author Contributions

Conceptualization, N.M.; resources, K.T.A.; writing—original draft preparation, N.M. and K.M.; writing—review and editing, N.M. and K.M.; supervision, K.T.A. and K.M.; funding acquisition, K.T.A. and K.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare that there is no conflicts of interest.

References

  1. Shwehdi, M.; Mohamed, S.R. Proposed smart DC nano-grid for green buildings—A reflective view. In Proceedings of the 2014 International Conference on Renewable Energy Research and Application (ICRERA), Milwaukee, WI, USA, 19–22 October 2014; pp. 765–769. [Google Scholar]
  2. Alam, S.; Al-Ismail, F.S.; Salem, A.; Abido, M.A. High-Level Penetration of Renewable Energy Sources Into Grid Utility: Challenges and Solutions. IEEE Access 2020, 8, 190277–190299. [Google Scholar] [CrossRef]
  3. Kant, K.; Gupta, O.H. DC Microgrid: A Comprehensive Review on Protection Challenges and Schemes. IETE Tech. Rev. 2022, 40, 574–590. [Google Scholar] [CrossRef]
  4. Parikh, U.B.; Das, B.; Maheshwari, R. Fault classification technique for series compensated transmission line using support vector machine. Int. J. Electr. Power Energy Syst. 2010, 32, 629–636. [Google Scholar] [CrossRef]
  5. Hoq, T.; Wang, J.; Taylor, N. The Impact of Current Inversion on Line Protection in High Voltage Transmission Lines with Series Compensation. In Proceedings of the 2019 Nordic Workshop on Power and Industrial Electronics (NORPIE), Narvik, Norway, 25–27 September 2019; pp. 1–7. [Google Scholar]
  6. Bakie, E.; Westhoff, C.; Fischer, N.; Bell, J. Voltage and current inversion challenges when protecting series-compensated lines —A case study. In Proceedings of the 2016 69th Annual Conference for Protective Relay Engineers (CPRE), College Station, TX, USA, 4–7 April 2016; pp. 1–14. [Google Scholar]
  7. Pilotto, L.; Bianco, A.; Long, W.; Edris, A.-A. Impact of TCSC control methodologies on subsynchronous oscillations. IEEE Trans. Power Deliv. 2003, 18, 243–252. [Google Scholar] [CrossRef]
  8. Adamiak, M.; Patterson, R. Protection requirements for flexible AC transmission systems. In Proceedings of the International Conference on Large High Voltage Electric Systems; International Conference on Large High Voltage Electric Systems: Paris, France, 1992; Volume 2, p. 34/206. [Google Scholar]
  9. Bhaskar, M.A.; Indhirani, A. Impact of FACTS devices on distance protection in transmission system. In Proceedings of the 2014 IEEE National Conference on Emerging Trends in New & Renewable Energy Sources and Energy Management (NCET NRES EM), Chennai, India, 16–17 December 2014; pp. 52–58. [Google Scholar]
  10. Mishra, S.; Gupta, S.; Yadav, A. Study on factors affecting distance protection scheme of UPFC compensated transmission lines. In Proceedings of the 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India, 3–5 January 2020; pp. 143–148. [Google Scholar]
  11. Khederzadeh, M. Power quality impacts of series and shunt compensated lines on digital protective relays. In Proceedings of the International Conference on Power Systems Transients, New Orleans, LA, USA, 28 September–2 October 2003. [Google Scholar]
  12. Keramat, M.M.; Fazaeli, M.H. The New Adaptive Protection Method for the Compensated Transmission Lines with the Series Capacitor in a High Share of Wind Energy Resources by Using PMU Data. In Proceedings of the 7th Iran Wind Energy Conference (IWEC2021), Shahrood, Iran, 17–18 May 2021; pp. 1–6. [Google Scholar]
  13. Habib, H.F.; Lashway, C.R.; Mohammed, O.A. A review of communication failure impacts on adaptive microgrid protection schemes and the use of energy storage as a contingency. IEEE Trans. Ind. Appl. 2017, 54, 1194–1207. [Google Scholar] [CrossRef]
  14. Cheruiyot, K.; Lengaram, E.; Siteleki, M. South Africa’s Energy Landscape Amidst the Crisis: Unpacking Energy Sources and Drivers with 2022 South African Census Data. Sustainability 2024, 16, 682. [Google Scholar] [CrossRef]
  15. Akinbami, O.M.; Oke, S.R.; Bodunrin, M.O. The state of renewable energy development in South Africa: An overview. Alex. Eng. J. 2021, 60, 5077–5093. [Google Scholar] [CrossRef]
  16. Energy, D.O. Integrated resource plan for electricity 2010–2030. Govern. Gaz. 2011, 551, 34263. [Google Scholar]
  17. Eskom. Just Energy Transition (JET)—Eskom. Available online: www.eskom.co.za/about-eskom/just-energy-transition-jet/ (accessed on 23 June 2024).
  18. Eskom. Current Installed Renewable Energy Capacity. Available online: https://www.eskom.co.za/dataportal/renewables-performance/renewable-statistics/ (accessed on 23 June 2024).
  19. Ndlela, N.W.; Davidson, I.E. Power planning for a smart integrated African super-grid. In Proceedings of the 2022 30th Southern African Universities Power Engineering Conference (SAUPEC), Durban, South Africa, 25–27 January 2022; pp. 1–6. [Google Scholar]
  20. Mukoni, E. Grid-connected Hybrid Energy System Modeling and Optimization Study for Green Hydrogen Production in South Africa. Master’s Thesis, Stellenbosch University, Cape town, South Africa, 2023. [Google Scholar]
  21. Eskom. Hourly Renewable Generation. Available online: https://www.eskom.co.za/dataportal/renewables-performance/hourly-renewable-generation/ (accessed on 23 June 2024).
  22. Department of Environment Affairs (DEA). Phase 2 Strategic Environmental Assessment for Wind and Solar pv Energy in South Africa. Available online: https://redzs.csir.co.za/ (accessed on 23 June 2024).
  23. Abasi, M.; Joorabian, M.; Saffarian, A.; Seifossadat, S.G. A Comprehensive Review of Various Fault Location Methods for Transmission Lines Compensated by FACTS devices and Series Capacitors. J. Oper. Autom. Power Eng. 2021, 9, 213–225. [Google Scholar]
  24. Biswas, S.; Nayak, P.K. State-of-the-art on the protection of FACTS compensated high-voltage transmission lines: A review. High Volt. 2018, 3, 21–30. [Google Scholar] [CrossRef]
  25. NKadandani, B.; Maiwada, Y.A. An overview of facts controllers for power quality improvement. Int. J. Eng. Sci. IJES 2015, 4, 9–17. [Google Scholar]
  26. Sunny, A.; Janamala, V. Available Transfer Capability (ATC) enhancement & optimization of UPFC shunt converter location with GSF in deregulated power system. In Proceedings of the 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, India, 18–19 March 2016; pp. 1–5. [Google Scholar]
  27. Biswas, S.; Nayak, P.K. A fault detection and classification scheme for unified power flow controller compensated transmission lines connecting wind farms. IEEE Syst. J. 2020, 15, 297–306. [Google Scholar] [CrossRef]
  28. Riahinasab, M.; Dehghani, M.; Fathollahi, A.; Behzadfar, M.R. A brief Overview of the Application of Unified Power Flow Controller in Power Systems. Int. J. Smart Electr. Eng. 2023, 12, 183–191. [Google Scholar]
  29. Rivera, C.S.; Messina, A.; Olguín, D.S.; Ruiz, V.D. Analysis of Subsynchronous Torsional, Interactions with SVCs. Electr. Power Compon. Syst. 2003, 31, 467–481. [Google Scholar] [CrossRef]
  30. Bhowmick, S. Flexible ac transmission systems (FACTS): Newton power-flow modeling of voltage-sourced converter-based controllers; CRC Press: Boca Raton, India, 2018. [Google Scholar]
  31. Fawzy, I.Y.; Mossa, M.A.; Elsawy, A.M.; Diab, A.A.Z. Enhancing the Performance of Power System under Abnormal Conditions Using Three Different FACTS Devices. Int. J. Robot. Control Syst. 2024, 4, 1–32. [Google Scholar]
  32. Gupta, S.; Tripathi, R.K. FACTS modelling and control: Application of CSC based STATCOM in transmission line. In Proceedings of the 2012 Students Conference on Engineering and Systems, Allahabad, India, 16–18 March 2012; pp. 1–5. [Google Scholar]
  33. Gangolu, S.; Sarangi, S.; Mohanty, R. Relay algorithm for STATCOM compensated line using differential current ratio. Int. J. Electr. Power Energy Syst. 2024, 155, 109473. [Google Scholar] [CrossRef]
  34. Prasad, C.D.; Biswal, M.; Ray, P. Line protection in presence of high penetration of wind energy: A review on possible solutions. Electr. Eng. 2024; 1–13. [Google Scholar] [CrossRef]
  35. Song, J.; Oh, S.; Lee, J.; Shin, J.; Jang, G. Application of the First Replica Controller in Korean Power Systems. Energies 2020, 13, 3343. [Google Scholar] [CrossRef]
  36. Hashemi, S.M.; Hagh, M.T.; Seyedi, H. High-speed relaying scheme for protection of transmission lines in the presence of thyristor-controlled series capacitor. IET Gener. Transm. Distrib. 2014, 8, 2083–2091. [Google Scholar] [CrossRef]
  37. Sidhu, T.; Khederzadeh, M. TCSC impact on communication-aided distance-protection schemes and its mitigation. Iee Proc. Gener. Transm. Distrib. 2005, 152, 714–728. [Google Scholar] [CrossRef]
  38. Paithankar, Y.G.; Bhide, S. Fundamentals of Power System Protection; PHI Learning Pvt. Ltd.: Delhi, India, 2022. [Google Scholar]
  39. Jacome, Y.; Henville, C. Setting Out-of-Step Blocking or Tripping Using Dynamic Simulations; Schweitzer Engineering Laboratories: Pullman, WA, USA, 2011. [Google Scholar]
  40. Tziouvaras, D.A.; Hou, D. Out-of-step protection fundamentals and advancements. In Proceedings of the 57th Annual Conference for Protective Relay Engineers, 2004, College Station, TX, USA, 1 April 2004; pp. 282–307. [Google Scholar]
  41. Arooj, Q.; Khan, U.A. Improving the Efficiency of Transmission Line by Using 48-Pulse Smart Power Flow Controller. In Proceedings of the 2021 16th International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, 22–23 December 2021; pp. 1–6. [Google Scholar]
  42. Singh, P.K.; Dahiya, A.K. Analysis Modelling & Simulation of VSC based D-Statcom for Reactive VAR Compensation. In Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, India, 1–3 March 2018; pp. 1–6. [Google Scholar]
  43. Dhekekar, R.S.; Srikanth, N.V. ANN Controlled VSC with Harmonic Reduction for VAR Control of Transmission Line. Int. J. Power Electron. Drive Syst. (IJPEDS) 2011, 2, 76–84. [Google Scholar] [CrossRef]
  44. Albatsh, F.M.; Ismail, B.; Awalin, L.J.; Alhamrouni, I.; Ahmad, S.; Naidu, K. Implementation of UPFC based on fuzzy logic controller to resolve power quality issues in transmission network. In Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI), Langkawi, Malaysia, 25–27 November 2017; pp. 1–6. [Google Scholar]
  45. Chawla, S.; Garg, S.; Ahuja, B. Optimal location of series-shunt FACTS device for transmission line compensation. In Proceedings of the 2009 International Conference on Control, Automation, Communication and Energy Conservation, Perundurai, India, 4–6 June 2009; pp. 1–6. [Google Scholar]
  46. Chethan, M.; Kuppan, R. A review of FACTS device implementation in power systems using optimization techniques. J. Eng. Appl. Sci. 2024, 71, 18. [Google Scholar] [CrossRef]
  47. Ooi, B.; Kazerani, M.; Marceau, R.; Wolanski, Z.; Galiana, F.; McGillis, D.; Joos, G. Mid-point siting of FACTS devices in transmission lines. IEEE Trans. Power Deliv. 1997, 12, 1717–1722. [Google Scholar] [CrossRef]
  48. Biswas, S.; Nayak, P.K. A New Approach for Protecting TCSC Compensated Transmission Lines Connected to DFIG-Based Wind Farm. IEEE Trans. Ind. Inform. 2020, 17, 5282–5291. [Google Scholar] [CrossRef]
  49. Amroune, M.; Zellagui, M.; Bouktir, T.; Chaghi, A. Optimal Placement of TCSC to Improve Voltage Stability Limit Considering Impacts on Setting Zones of Distance Protection Relays. Acta Electroteh. 2014, 55, 10–18. [Google Scholar]
  50. Das, S.; Panigrahi, B.K.; Jaiswal, P.K. Qualitative Assessment of Power Swing for Enhancing Security of Distance Relay in a TCSC-Compensated Line. IEEE Trans. Power Deliv. 2020, 36, 223–234. [Google Scholar] [CrossRef]
  51. Mithulananthan, N.; Canizares, C.; Reeve, J.; Rogers, G. Comparison of PSS, SVC, and STATCOM controllers for damping power system oscillations. IEEE Trans. Power Syst. 2003, 18, 786–792. [Google Scholar] [CrossRef]
  52. Murali, D.; Rajaram, M.; Reka, N. Comparison of FACTS Devices for Power System Stability Enhancement. Int. J. Comput. Appl. 2010, 8, 30–35. [Google Scholar] [CrossRef]
  53. Ara, A.L.; Kazemi, A.; Niaki, S.N. Modelling of Optimal Unified Power Flow Controller (OUPFC) for optimal steady-state performance of power systems. Energy Convers. Manag. 2011, 52, 1325–1333. [Google Scholar]
  54. Kumkratug, P.; Haque, M. Versatile model of a unified power flow controller in a simple power system. Iee Proc. Gener. Transm. Distrib. 2003, 150, 155–161. [Google Scholar] [CrossRef]
  55. Vittal, V.; Bhatia, N.; Fouad, A. Analysis of the inter-area mode phenomenon in power systems following large disturbances. IEEE Trans. Power Syst. 1991, 6, 1515–1521. [Google Scholar] [CrossRef]
  56. Tzelepis, D.; Tsotsopoulou, E.; Nikolaidis, V.; Dysko, A.; Papaspiliotopoulos, V.; Hong, Q.; Booth, C. Impact of Synchronous Condensers on Transmission Line Protection in Scenarios with High Penetration of Renewable Energy Sources. In Proceedings of the 15th International Conference on Developments in Power System Protection (DPSP 2020), Liverpool, UK, 9–12 March 2020. [Google Scholar]
  57. Sa-Ngawong, N.; Ngamroo, I. Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control. Renew. Energy 2015, 74, 555–567. [Google Scholar] [CrossRef]
  58. Hill, C.A.; Such, M.C.; Chen, D.; Gonzalez, J.; Grady, W.M. Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation. IEEE Trans. Smart Grid 2012, 3, 850–857. [Google Scholar] [CrossRef]
  59. Kakimoto, N.; Takayama, S.; Satoh, H.; Nakamura, K. Power Modulation of Photovoltaic Generator for Frequency Control of Power System. IEEE Trans. Energy Convers. 2009, 24, 943–949. [Google Scholar] [CrossRef]
  60. Xin, H.; Liu, Y.; Wang, Z.; Gan, D.; Yang, T. A New Frequency Regulation Strategy for Photovoltaic Systems Without Energy Storage. IEEE Trans. Sustain. Energy 2013, 4, 985–993. [Google Scholar] [CrossRef]
  61. Zarina, P.; Mishra, S.; Sekhar, P. Exploring frequency control capability of a PV system in a hybrid PV-rotating machine-without storage system. Int. J. Electr. Power Energy Syst. 2014, 60, 258–267. [Google Scholar] [CrossRef]
  62. Alatrash, H.; Mensah, A.; Mark, E.; Haddad, G.; Enslin, J. Generator emulation controls for photovoltaic inverters. IEEE Trans. Smart Grid 2012, 3, 996–1011. [Google Scholar] [CrossRef]
  63. Nanou, S.I.; Papakonstantinou, A.G.; Papathanassiou, S.A. A generic model of two-stage grid-connected PV systems with primary frequency response and inertia emulation. Electr. Power Syst. Res. 2015, 127, 186–196. [Google Scholar] [CrossRef]
  64. Ntuli, W.K.; Sharma, G.; Kabeya, M. Study of Fault Ride-Through Capability of Doubly Fed Induction Generator Based Wind Turbine. In Proceedings of the 2022 30th Southern African Universities Power Engineering Conference (SAUPEC), Durban, South Africa, 25–27 January 2022; pp. 1–6. [Google Scholar]
  65. Liang, X.; Andalib-Bin-Karim, C. Harmonics and mitigation techniques through advanced control in grid-connected renewable energy sources: A review. IEEE Trans. Ind. Appl. 2018, 54, 3100–3111. [Google Scholar] [CrossRef]
  66. Reddy, T.D.; Dash, J.R.; Agarwal, P. A New Control Strategy of a Single Stage PV System for Providing Frequency Support to the Power Grid. In Proceedings of the 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET), London, UK, 19–21 May 2023; pp. 1–5. [Google Scholar]
  67. Gevorgian, V.; Zhang, Y.; Ela, E. Investigating the Impacts of Wind Generation Participation in Interconnection Frequency Response. IEEE Trans. Sustain. Energy 2014, 6, 1004–1012. [Google Scholar] [CrossRef]
  68. Tafti, H.D.; Konstantinou, G.; Lei, Q.; Fletcher, J.E.; Farivar, G.G.; Ceballos, S.; Pou, J. Adaptive power system frequency support from distributed photovoltaic systems. Sol. Energy 2023, 257, 231–239. [Google Scholar] [CrossRef]
  69. Craciun, B.-I.; Kerekes, T.; Sera, D.; Teodorescu, R. Frequency Support Functions in Large PV Power Plants With Active Power Reserves. IEEE J. Emerg. Sel. Top. Power Electron. 2014, 2, 849–858. [Google Scholar] [CrossRef]
  70. Peng, Q.; Tang, Z.; Yang, Y.; Liu, T.; Blaabjerg, F. Event-Triggering Virtual Inertia Control of PV Systems With Power Reserve. IEEE Trans. Ind. Appl. 2021, 57, 4059–4070. [Google Scholar] [CrossRef]
  71. Seneviratne, C.; Ozansoy, C. Frequency response due to a large generator loss with the increasing penetration of wind/PV generation—A literature review. Renew. Sustain. Energy Rev. 2016, 57, 659–668. [Google Scholar] [CrossRef]
  72. Alam, M.J.E.; Muttaqi, K.M.; Sutanto, D. A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing. IEEE Trans. Energy Convers. 2014, 29, 507–518. [Google Scholar]
  73. Xu, Q.; Wen, H.; Zhu, Y.; Li, X. An Adaptive Ramp-Rate Control for Photovoltaic System to Mitigate Output Fluctuation. In Proceedings of the 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Macao, China, 1–4 December 2019; pp. 1–5. [Google Scholar]
  74. Patel, S.; Ahmed, M.; Kamalasadan, S. A Novel Energy Storage-Based Net-Load Smoothing and Shifting Architecture for High Amount of Photovoltaics Integrated Power Distribution System. IEEE Trans. Ind. Appl. 2020, 56, 3090–3099. [Google Scholar] [CrossRef]
  75. Tran, V.T.; Islam, M.R.; Sutanto, D.; Muttaqi, K.M. Mitigation of solar PV intermittency using ramp-rate control of energy buffer unit. IEEE Trans. Energy Convers. 2018, 34, 435–445. [Google Scholar] [CrossRef]
  76. Bao, G.; Tan, H.; Ding, K.; Ma, M.; Wang, N. A Novel Photovoltaic Virtual Synchronous Generator Control Technology Without Energy Storage Systems. Energies 2019, 12, 2240. [Google Scholar] [CrossRef]
  77. Marzebali, M.H.; Mohiti, M. An adaptive droop-based control strategy for fuel cell-battery hybrid energy storage system to support primary frequency in stand-alone microgrids. J. Energy Storage 2019, 27, 101127. [Google Scholar] [CrossRef]
  78. Yang, T.; Mok, K.T.; Tan, S.-C.; Lee, C.-K.; Hui, S.-Y.R. Electric Springs with Coordinated Battery Management for Reducing Voltage and Frequency Fluctuations in Microgrids. IEEE Trans. Smart Grid 2016, 9, 1943–1952. [Google Scholar] [CrossRef]
  79. Zhu, Y.; Wen, H.; Chu, G. Active power control for grid-connected photovoltaic system: A review. In Proceedings of the 2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Weihai, China, 13–15 July 2020; pp. 1506–1511. [Google Scholar]
  80. DehghaniTafti, H.; Konstantinou, G.; Fletcher, J.E.; Callegaro, L.; Farivar, G.G.; Pou, J. Control of Distributed Photovoltaic Inverters for Frequency Support and System Recovery. IEEE Trans. Power Electron. 2021, 37, 4742–4750. [Google Scholar] [CrossRef]
  81. Ma, H.; Chowdhury, B. Working towards frequency regulation with wind plants: Combined control approaches. IET Renew. Power Gener. 2010, 4, 308–316. [Google Scholar] [CrossRef]
  82. Zarei, M.E.; Ramirez, D.; Prodanovic, M.; Venkataramanan, G. Multivector model predictive power control for grid connected converters in renewable power plants. IEEE J. Emerg. Sel. Top. Power Electron. 2021, 10, 1466–1478. [Google Scholar] [CrossRef]
  83. Kumar, V.; Sharma, V.; Naresh, R. Leader Harris Hawks algorithm based optimal controller for automatic generation control in PV-hydro-wind integrated power network. Electr. Power Syst. Res. 2023, 214, 108924. [Google Scholar] [CrossRef]
  84. Liu, Y.; Wang, H.; Wang, X.; Guo, G.; Jing, H. Control Strategy and Corresponding Parameter Analysis of a Virtual Synchronous Generator Considering Frequency Stability of Wind Power Grid-Connected System. Electronics 2022, 11, 2806. [Google Scholar] [CrossRef]
  85. Liu, J.; Liu, J. Modeling and Calculation of Grid Frequency Support Effect and Transient Energy Demand of a Virtual Synchronous Generator. In Proceedings of the 2022 International Power Electronics Conference (IPEC-Himeji 2022-ECCE Asia), Himeji, Japan, 15–19 May 2022; pp. 2334–2339. [Google Scholar]
  86. Peng, Q.; Yang, Y.; Liu, T.; Blaabjerg, F. Coordination of virtual inertia control and frequency damping in PV systems for optimal frequency support. CPSS Trans. Power Electron. Appl. 2020, 5, 305–316. [Google Scholar] [CrossRef]
  87. Kabsha, M.M.; Rather, Z.H. Adaptive Control Strategy for Frequency Support From MTDC Connected Offshore Wind Power Plants. IEEE Trans. Power Electron. 2022, 38, 3981–3991. [Google Scholar] [CrossRef]
  88. Dozein, M.G.; De Corato, A.M.; Mancarella, P. Virtual inertia response and frequency control ancillary services from hydrogen electrolyzers. IEEE Trans. Power Syst. 2022, 38, 2447–2459. [Google Scholar] [CrossRef]
  89. Azamian, A.; Rezaeealam, B.; Ghanbari, T.; Rokrok, E. Improved Low Voltage Ride-through Capability of PV Connected to the Unbalanced Main Grid. J. Sol. Energy Res. 2023, 8, 1326–1344. [Google Scholar]
  90. Firouzi, M.; Gharehpetian, G.B. Improving Fault Ride-Through Capability of Fixed-Speed Wind Turbine by Using Bridge-Type Fault Current Limiter. IEEE Trans. Energy Convers. 2013, 28, 361–369. [Google Scholar] [CrossRef]
  91. Sitharthan, R.; Sundarabalan, C.K.; Devabalaji, K.R.; Nataraj, S.K.; Karthikeyan, M. Improved fault ride through capability of DFIG-wind turbines using customized dynamic voltage restorer. Sustain. Cities Soc. 2018, 39, 114–125. [Google Scholar]
  92. Falehi, A.; Rafiee, M. Enhancement of DFIG-Wind Turbine’s LVRT capability using novel DVR based Odd-nary Cascaded Asymmetric Multi-Level Inverter. Eng. Sci. Technol. Int. J. 2017, 20, 805–824. [Google Scholar] [CrossRef]
  93. Roy, T.K.; Mahmud, M.A.; Oo, A.M.T. Nonlinear Backstepping Controller Design for Improving Fault Ride Through Capabilities of DFIG-Based Wind Farms. In Proceedings of the 2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 5–10 August 2018; pp. 1–5. [Google Scholar]
  94. Bekiroglu, E.; Yazar, M.D. Improving Fault Ride Through Capability of DFIG with Fuzzy Logic Controlled Crowbar Protection. In Proceedings of the 2022 11th International Conference on Renewable Energy Research and Application (ICRERA), Istanbul, Turkey, 18–21 September 2022; pp. 374–378. [Google Scholar]
  95. Alam, S.; Abido, M.A.Y.; Hussein, A.E.-D.; El-Amin, I. Fault Ride through Capability Augmentation of a DFIG-Based Wind Integrated VSC-HVDC System with Non-Superconducting Fault Current Limiter. Sustainability 2019, 11, 1232. [Google Scholar] [CrossRef]
  96. Telukunta, V.; Pradhan, J.; Agrawal, A.; Singh, M.; Srivani, S.G. Protection challenges under bulk penetration of renewable energy resources in power systems: A review. CSEE J. Power Energy Syst. 2017, 3, 365–379. [Google Scholar] [CrossRef]
  97. Meenakshi; Kale, V.; Khond, S. Adaptive Overcurrent Protection Scheme for Distribution Network Integrated with Distributed Energy Resources. In Proceedings of the 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Jaipur, India, 14–17 December 2022; pp. 1–5. [Google Scholar]
  98. Shah, H.; Chakravorty, J.; Chothani, N.G. Protection challenges and mitigation techniques of power grid integrated to renewable energy sources: A review. Energy Sources Part A: Recover. Util. Environ. Eff. 2023, 45, 4195–4210. [Google Scholar] [CrossRef]
  99. Dubey, R.; Samantaray, S.R.; Panigrahi, B.K. Simultaneous impact of unified power flow controller and off-shore wind penetration on distance relay characteristics. IET Gener. Transm. Distrib. 2014, 8, 1869–1880. [Google Scholar] [CrossRef]
  100. Fang, Y.; Jia, K.; Yang, Z.; Li, Y.; Bi, T. Impact of inverter-interfaced renewable energy generators on distance protection and an improved scheme. IEEE Trans. Ind. Electron. 2018, 66, 7078–7088. [Google Scholar] [CrossRef]
  101. George, N.; Naidu, O. Distance protection issues with renewable power generators and possible solutions. In Proceedings of the 16th International Conference on Developments in Power System Protection (DPSP 2022), Newcastle, UK, 7–10 March 2022; Volume 2022: IET, pp. 373–378. [Google Scholar]
  102. Hooshyar, A.; Azzouz, M.A.; El-Saadany, E.F. Distance Protection of Lines Emanating From Full-Scale Converter-Interfaced Renewable Energy Power Plants—Part II: Solution Description and Evaluation. IEEE Trans. Power Deliv. 2014, 30, 1781–1791. [Google Scholar] [CrossRef]
  103. Wang, C.; Song, G.; Tang, J. Protection performance of traditional distance relays under wind power integration. In Proceedings of the 13th International Conference on Development in Power System Protection 2016 (DPSP), Edinburgh, UK, 7 March 2015–10 March 2016. [Google Scholar]
  104. Chen, X.; Yin, X.; Zhang, Z. Impacts of DFIG-based wind farm integration on its tie line distance protection and countermeasures. IEEJ Trans. Electr. Electron. Eng. 2017, 12, 553–564. [Google Scholar] [CrossRef]
  105. Chen, Y.; Wen, M.; Yin, X.; Cai, Y.; Zheng, J. Distance protection for transmission lines of DFIG-based wind power integration system. Int. J. Electr. Power Energy Syst. 2018, 100, 438–448. [Google Scholar] [CrossRef]
  106. Kumar, S.; Gupta, A.; Bindal, R.K. Power quality investigation of a grid tied hybrid energy system using a D-STATCOM control and grasshopper optimization technique. Results Control. Optim. 2024, 14, 100368. [Google Scholar] [CrossRef]
  107. Ma, J.; Zhang, W.; Liu, J.; Thorp, J.S. A novel adaptive distance protection scheme for DFIG wind farm collector lines. Int. J. Electr. Power Energy Syst. 2018, 94, 234–244. [Google Scholar] [CrossRef]
  108. Sadeghi, H. A novel method for adaptive distance protection of transmission line connected to wind farms. Int. J. Electr. Power Energy Syst. 2012, 43, 1376–1382. [Google Scholar] [CrossRef]
  109. Jodaei, A.; Moravej, Z.; Pazoki, M. Effective protection scheme for transmission lines connected to large scale photovoltaic power plants. Electr. Power Syst. Res. 2024, 228, 110103. [Google Scholar] [CrossRef]
  110. Prasad, C.D.; Biswal, M.; Abdelaziz, A.Y. Adaptive differential protection scheme for wind farm integrated power network. Electr. Power Syst. Res. 2020, 187, 106452. [Google Scholar] [CrossRef]
  111. Mohamed, A.A.R.; Sharaf, H.M.; Ibrahim, D.K. Enhancing Distance Protection of Long Transmission Lines Compensated with TCSC and Connected With Wind Power. IEEE Access 2021, 9, 46717–46730. [Google Scholar] [CrossRef]
  112. Ma, J.; Ma, W.; Qiu, Y.; Thorp, J.S. An Adaptive Distance Protection Scheme Based on the Voltage Drop Equation. IEEE Trans. Power Deliv. 2015, 30, 1931–1940. [Google Scholar] [CrossRef]
  113. Ghorbani, A.; Khederzadeh, M.; Mozafari, B. Impact of SVC on the protection of transmission lines. Int. J. Electr. Power Energy Syst. 2012, 42, 702–709. [Google Scholar] [CrossRef]
  114. Singh, A.R.; Dambhare, S.S. Adaptive distance protection of transmission line in presence of SVC. Int. J. Electr. Power Energy Syst. 2013, 53, 78–84. [Google Scholar] [CrossRef]
  115. Girgis, A.; Sallam, A.; El-Din, A. An adaptive protection scheme for advanced series compensated (ASC) transmission lines. IEEE Trans. Power Deliv. 1998, 13, 414–420. [Google Scholar] [CrossRef]
  116. Dubey, R.; Samantaray, S.R.; Panigrahi, B.K.; Venkoparao, V.G. Data-mining model based adaptive protection scheme to enhance distance relay performance during power swing. Int. J. Electr. Power Energy Syst. 2016, 81, 361–370. [Google Scholar] [CrossRef]
  117. Ghorbani, A. An adaptive distance protection scheme in the presence of phase shifting transformer. Electr. Power Syst. Res. 2015, 129, 170–177. [Google Scholar] [CrossRef]
  118. Lin, H.; Sun, K.; Tan, Z.; Liu, C.; Guerrero, J.M.; Vasquez, J.C. Adaptive protection combined with machine learning for microgrids. IET Gener. Transm. Distrib. 2019, 13, 770–779. [Google Scholar] [CrossRef]
  119. Mehrjerdi, H.; Ghorbani, A. Adaptive algorithm for transmission line protection in the presence of UPFC. Int. J. Electr. Power Energy Syst. 2017, 91, 10–19. [Google Scholar] [CrossRef]
  120. Paladhi, S.; Pradhan, A.K. Adaptive Distance Protection for Lines Connecting Converter-Interfaced Renewable Plants. IEEE J. Emerg. Sel. Top. Power Electron. 2020, 9, 7088–7098. [Google Scholar] [CrossRef]
  121. Uzubi, U.; Ekwue, A.; Ejiogu, E. An adaptive distance protection scheme for high varying fault resistances: Updated results. Sci. Afr. 2020, 9, e00528. [Google Scholar] [CrossRef]
  122. Uzubi, U.; Ekwue, A.; Ejiogu, E. Adaptive distance relaying: Solution to challenges of conventional protection schemes in the presence of remote infeeds. Int. Trans. Electr. Energy Syst. 2020, 30, e12330. [Google Scholar] [CrossRef]
  123. Abdelhamid, M.; Kamel, S.; Ahmed, E.M.; Agyekum, E.B. An Adaptive Protection Scheme Based on a Modified Heap-Based Optimizer for Distance and Directional Overcurrent Relays Coordination in Distribution Systems. Mathematics 2022, 10, 419. [Google Scholar] [CrossRef]
  124. Dewangan, F.; Biswal, M. An adaptive time–frequency approach based secured back-up distance protection scheme for system stress conditions. Sustain. Energy Grids Netw. 2022, 31, 100732. [Google Scholar] [CrossRef]
  125. Khoshbouy, M.; Yazdaninejadi, A.; Bolandi, T.G. Transmission line adaptive protection scheme: A new fault detection approach based on pilot superimposed impedance. Int. J. Electr. Power Energy Syst. 2021, 137, 107826. [Google Scholar] [CrossRef]
  126. Bharathidasan, S.; Sankar, M.; Akash, S. Adaptive Distance Protection for Smart Grids with Infeed Compensation using Synchronized Phasor Measurements. In Proceedings of the 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 23–25 December 2022; pp. 1–10. [Google Scholar]
  127. Chao, C.; Zheng, X.; Weng, Y.; Liu, Y.; Gao, P.; Tai, N. Adaptive Distance Protection Based on the Analytical Model of Additional Impedance for Inverter-Interfaced Renewable Power Plants During Asymmetrical Faults. IEEE Trans. Power Deliv. 2021, 37, 3823–3834. [Google Scholar] [CrossRef]
  128. Teimourzadeh, H.T.; Mohammadi-Ivatloo, B.; Shahidehpour, M. Adaptive Protection of Partially Coupled Transmission Lines. IEEE Trans. Power Deliv. 2020, 36, 429–440. [Google Scholar] [CrossRef]
  129. Abo-Hamad, G.M.; Ibrahim, D.K.; Zahab, E.A.; Zobaa, A.F. Adaptive Mho Distance Protection for Interconnected Transmission Lines Compensated with Thyristor Controlled Series Capacitor. Energies 2021, 14, 2477. [Google Scholar] [CrossRef]
  130. Raju, B.M.; S, A. Adaptive Quadrilateral Setting for Distance Protection of Feeder Connecting Renewable Energy. In Proceedings of the 2021 IEEE Kansas Power and Energy Conference (KPEC), Manhattan, KS, USA, 19–20 April 2021; pp. 1–6. [Google Scholar]
  131. Peng, F.; Gao, H.; Miao, W.; Feng, X.; Xu, B.; Wu, Y. Analysis of Fault Characteristics and Distance Protection Adaptability for VSC-HVDC-Connected Offshore Wind Farms. In Proceedings of the 2023 5th Asia Energy and Electrical Engineering Symposium (AEEES), Chengdu, China, 23–26 March 2023; pp. 572–577. [Google Scholar]
  132. Li, B.; Sheng, Y.; He, J.; Li, Y.; Xie, Z.; Cao, Y. Improved distance protection for wind farm transmission line based on dynamic frequency estimation. Int. J. Electr. Power Energy Syst. 2023, 153, 109382. [Google Scholar] [CrossRef]
  133. Jodaei, A.; Moravej, Z. Fault Detection and Location in Power Systems with Large-Scale Photovoltaic Power Plant by Adaptive Distance Relays. Iran. J. Energy Environ. 2024, 15, 265–278. [Google Scholar] [CrossRef]
  134. Mallat, S. A Wavelet Tour of Signal Processing; Elsevier: Amsterdam, The Netherlands, 1999. [Google Scholar]
  135. Dash, P.; Panigrahi, B.; Panda, G. Power quality analysis using s-transform. IEEE Trans. Power Deliv. 2003, 18, 406–411. [Google Scholar] [CrossRef]
  136. Goli, R.K.; Shaik, A.G.; Ram, S.T. A transient current based double line transmission system protection using fuzzy-wavelet approach in the presence of UPFC. Int. J. Electr. Power Energy Syst. 2015, 70, 91–98. [Google Scholar] [CrossRef]
  137. Kumar, B.; Yadav, A. Wavelet singular entropy approach for fault detection and classification of transmission line compensated with UPFC. In Proceedings of the 2016 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India, 25–26 February 2016; pp. 1–6. [Google Scholar]
  138. Samantaray, S.; Tripathy, L.; Dash, P. A new cross-differential protection scheme for parallel transmission lines including UPFC. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17–21 July 2016; p. 1. [Google Scholar]
  139. Moravej, Z.; Pazoki, M.; Khederzadeh, M. New Pattern-Recognition Method for Fault Analysis in Transmission Line With UPFC. IEEE Trans. Power Deliv. 2014, 30, 1231–1242. [Google Scholar] [CrossRef]
  140. Samantaray, S. A data-mining model for protection of FACTS-based transmission line. IEEE Trans. Power Deliv. 2013, 28, 612–618. [Google Scholar] [CrossRef]
  141. Baghaee, H.R.; Mlakic, D.; Nikolovski, S.; Dragicevic, T. Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks. IEEE J. Emerg. Sel. Top. Power Electron. 2019, 8, 2385–2403. [Google Scholar] [CrossRef]
  142. Liu, T.; Luo, H.; Kaynak, O.; Yin, S. A Novel Control-Performance-Oriented Data-Driven Fault Classification Approach. IEEE Syst. J. 2019, 14, 1830–1839. [Google Scholar] [CrossRef]
  143. Jena, M.K.; Samantaray, S.R. Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms. IEEE Trans. Neural Netw. Learn. Syst. 2015, 27, 8–17. [Google Scholar] [CrossRef]
  144. Pazoki, M.; Moravej, Z.; Khederzadeh, M.; Nair, N.-K.C. Effect of UPFC on protection of transmission lines with infeed current. Int. Trans. Electr. Energy Syst. 2016, 26, 2385–2401. [Google Scholar] [CrossRef]
  145. Kumar, B.; Yadav, A. Backup protection scheme for transmission line compensated with UPFC during high impedance faults and dynamic situations. IET Sci. Meas. Technol. 2017, 11, 703–712. [Google Scholar] [CrossRef]
  146. Biswas, S.; Nayak, P.K. Superimposed Component-Based Protection Scheme for UPFC Compensated Transmission Lines. In Proceedings of the 2018 20th National Power Systems Conference (NPSC), Tiruchirappalli, India, 14–16 December 2018; pp. 1–6. [Google Scholar]
  147. Kong, X.; Yuan, Y.; Gao, L.; Li, P.; Li, Q.; Shi, M.; Zhang, L. A Three-Zone Distance Protection Scheme Capable to Cope With the Impact of UPFC. IEEE Trans. Power Deliv. 2017, 33, 949–959. [Google Scholar] [CrossRef]
  148. Jiang, Y.; Yin, S. Recent Advances in Key-Performance-Indicator Oriented Prognosis and Diagnosis With a MATLAB Toolbox: DB-KIT. IEEE Trans. Ind. Inform. 2018, 15, 2849–2858. [Google Scholar] [CrossRef]
  149. Tripathy, L.; Dash, P.; Samantaray, S. A new cross-differential protection scheme for parallel transmission lines including UPFC. IEEE Trans. Power Deliv. 2013, 29, 1822–1830. [Google Scholar]
  150. Pavlatos, C.; Vita, V.; Ekonomou, L. Syntactic pattern recognition of power system signals. In Proceedings of the 19th WSEAS International Conference on Systems (Part of CSCC’15), Zakynthos Island, Greece, July 2015; pp. 16–20. [Google Scholar]
  151. Yusuff, A.A.; Fei, C.; Jimoh, A.; Munda, J.L. Fault location in a series compensated transmission line based on wavelet packet decomposition and support vector regression. Electr. Power Syst. Res. 2011, 81, 1258–1265. [Google Scholar] [CrossRef]
  152. Chen, K.; Huang, C.; He, J. Fault detection, classification and location for transmission lines and distribution systems: A review on the methods. High Volt. 2016, 1, 25–33. [Google Scholar] [CrossRef]
  153. Ha, H.; Han, S.; Lee, J. Fault Detection on Transmission Lines Using a Microphone Array and an Infrared Thermal Imaging Camera. IEEE Trans. Instrum. Meas. 2011, 61, 267–275. [Google Scholar] [CrossRef]
  154. Noori, M.R.; Shahrtash, S.M. Combined Fault Detector and Faulted Phase Selector for Transmission Lines Based on Adaptive Cumulative Sum Method. IEEE Trans. Power Deliv. 2013, 28, 1779–1787. [Google Scholar] [CrossRef]
  155. Ola, S.R.; Saraswat, A.; Goyal, S.K.; Sharma, V.; Khan, B.; Mahela, O.P.; Alhelou, H.H.; Siano, P. Alienation Coefficient and Wigner Distribution Function Based Protection Scheme for Hybrid Power System Network with Renewable Energy Penetration. Energies 2020, 13, 1120. [Google Scholar] [CrossRef]
  156. Das, B.; Reddy, J. Fuzzy-Logic-Based Fault Classification Scheme for Digital Distance Protection. IEEE Trans. Power Deliv. 2005, 20, 609–616. [Google Scholar] [CrossRef]
  157. Sahoo, B.; Samantaray, S.R. An enhanced travelling wave-based fault detection and location estimation technique for series compensated transmission network. In Proceedings of the 2017 7th International Conference on Power Systems (ICPS), Pune, India, 21–23 December 2017; pp. 61–68. [Google Scholar]
  158. Biswal, S.; Biswal, M.; Malik, O.P. Hilbert Huang Transform Based Online Differential Relay Algorithm for a Shunt-Compensated Transmission Line. IEEE Trans. Power Deliv. 2018, 33, 2803–2811. [Google Scholar] [CrossRef]
  159. Chatterjee, B.; Debnath, S. Sequence component based approach for fault discrimination and fault location estimation in UPFC compensated transmission line. Electr. Power Syst. Res. 2019, 180, 106155. [Google Scholar] [CrossRef]
  160. Adly, A.R.; Ali, Z.M.; Elsadd, M.A.; Mageed, H.M.A.; Aleem, S.H.A. An integrated scheme for a directional relay in the presence of a series-compensated line. Int. J. Electr. Power Energy Syst. 2020, 120, 106024. [Google Scholar] [CrossRef]
  161. Yang, Z.; Jia, K.; Fang, Y.; Zhu, Z.; Yang, B.; Bi, T. High-frequency fault component-based distance protection for large renewable power plants. IEEE Trans. Power Electron. 2020, 35, 10352–10362. [Google Scholar] [CrossRef]
  162. Barati, J.; Seifossadat, S.G.; Joorabian, M. A new adaptive coordination scheme of distance relays in DFIG—based wind farm collector lines and transmission line compensated by STATCOM. Int. Trans. Electr. Energy Syst. 2021, 31, e13205. [Google Scholar] [CrossRef]
  163. Swetapadma, A.; Yadav, A. A Novel Decision Tree Regression-Based Fault Distance Estimation Scheme for Transmission Lines. IEEE Trans. Power Deliv. 2016, 32, 234–245. [Google Scholar] [CrossRef]
  164. Sahani, M.; Dash, P. Fault location estimation for series-compensated double-circuit transmission line using EWT and weighted RVFLN. Eng. Appl. Artif. Intell. 2020, 88, 103336. [Google Scholar] [CrossRef]
  165. Chen, K.; Hu, J.; He, J. Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder. IEEE Trans. Smart Grid 2016, 9, 1748–1758. [Google Scholar]
  166. Saffarian, A.; Abasi, M. Fault location in series capacitor compensated three-terminal transmission lines based on the analysis of voltage and current phasor equations and asynchronous data transfer. Electr. Power Syst. Res. 2020, 187, 106457. [Google Scholar] [CrossRef]
  167. Kang, N.; Chen, J.; Liao, Y. A Fault-Location Algorithm for Series-Compensated Double-Circuit Transmission Lines Using the Distributed Parameter Line Model. IEEE Trans. Power Deliv. 2014, 30, 360–367. [Google Scholar] [CrossRef]
  168. Swetapadma, A.; Yadav, A.; Abdelaziz, A.Y. Intelligent schemes for fault classification in mutually coupled series-compensated parallel transmission lines. Neural Comput. Appl. 2020, 32, 6939–6956. [Google Scholar] [CrossRef]
  169. Al Kharusi, K.; El Haffar, A.; Mesbah, M. Supervised Machine Learning-Based Protection for Transmission Line Connected to PV plant. In Proceedings of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, Mauritius, 7–8 October 2021; pp. 1–6. [Google Scholar]
  170. Chen, Y.Q.; Fink, O.; Sansavini, G. Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis With Integrated Feature Extraction. IEEE Trans. Ind. Electron. 2017, 65, 561–569. [Google Scholar] [CrossRef]
  171. Al-Mohammed, A.H.; Abido, M.A. A Fully Adaptive PMU-Based Fault Location Algorithm for Series-Compensated Lines. IEEE Trans. Power Syst. 2014, 29, 2129–2137. [Google Scholar] [CrossRef]
  172. Zeinhom, A.N. Phasor measurement unit-based distance protection & fault location algorithm for series-compensated transmission lines. In Proceedings of the 2014 Saudi Arabia Smart Grid Conference (SASG), Jeddah, Saudi Arabia, 14–17 December 2014; pp. 1–7. [Google Scholar]
  173. Mishra, S.; Gupta, S.; Yadav, A. A novel two-terminal fault location approach utilizing traveling-waves for series compensated line connected to wind farms. Electr. Power Syst. Res. 2021, 198, 107362. [Google Scholar] [CrossRef]
  174. Khalili, M.; Namdari, F.; Rokrok, E. A Novel Protection Method for UPFC Compensated Transmission Line Based on Cooperative Game Theory. Iran. J. Electr. Electron. Eng. 2022, 18, 2082. [Google Scholar]
  175. Sadhu, R.; Paul, R. Classification of Faults in UPFC Compensated Transmission Line using MDL selected Wavelet based DWT Technique. In Proceedings of the 2022 International Interdisciplinary Conference on Mathematics, Engineering and Science (MESIICON), Durgapur, India, 11–12 November 2022; pp. 1–5. [Google Scholar]
  176. Elmitwally, A.; Ghanem, A. Local current-based method for fault identification and location on series capacitor-compensated transmission line with different configurations. Int. J. Electr. Power Energy Syst. 2021, 133, 107283. [Google Scholar] [CrossRef]
Figure 1. The status of installed RES in South Africa [19].
Figure 1. The status of installed RES in South Africa [19].
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Figure 2. Eskom’s hourly renewable energy generation [21].
Figure 2. Eskom’s hourly renewable energy generation [21].
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Figure 3. South African renewable energy development zones [22].
Figure 3. South African renewable energy development zones [22].
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Figure 4. Transmission line compensation.
Figure 4. Transmission line compensation.
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Figure 5. Summary of different techniques to improve fault ride-through capabilities in RES.
Figure 5. Summary of different techniques to improve fault ride-through capabilities in RES.
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Figure 6. Control strategies used to improve power quality in RES.
Figure 6. Control strategies used to improve power quality in RES.
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Figure 7. Technique used to improve frequency response of grid connected RES.
Figure 7. Technique used to improve frequency response of grid connected RES.
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Figure 8. The impacts of RES penetration on protection schemes.
Figure 8. The impacts of RES penetration on protection schemes.
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Figure 9. A simplified framework for fault detection, classification, and location [152].
Figure 9. A simplified framework for fault detection, classification, and location [152].
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Table 1. Renewable energy statistics as of 23 June 2024.
Table 1. Renewable energy statistics as of 23 June 2024.
Indicator Current Installed Capacity (MWh)
CPS500.00
PV2287.1
Wind 3442.6
Total 6280.2
Table 2. Annual contribution of renewable energy contribution based on operator’s data.
Table 2. Annual contribution of renewable energy contribution based on operator’s data.
YearIndicator CPSPVWindTotal
All Annual energy1,670,5405,095,75311,576,70918,186,108
2023–2024Total energy1,307,0485,095,75311,576,70918,186,108
2024–2025Total Energy 229,474941,5932,382,8773,615,973
Table 3. Summary of problems associated with adding FACTs devices.
Table 3. Summary of problems associated with adding FACTs devices.
Reference The Problem Solved Contributions
[48]The typical fault characteristics of DFIG and TCSC affect the performance of conventional distance relays.This article, a new relaying algorithm is proposed that utilizes sign of the half-cycle superimposed positive-sequence current for fault detection, empirical mode decomposition assisted random forest classifier for fault classification and modified impedance approach for fault location estimation.
[31]To investigation the impact of sub-synchronous torsional interactions, an analytical tool is developed.An advanced linear state model of the power system is developed that allows detailed evaluation of the impact of FACTS controllers for sub synchronous torsional interactions (SSTIs) between turbine-generator rotors and static VAR compensators (SVCs) in longitudinal power systems.
[49] Optimal placement of TCSC to improve voltage stability limit considering impacts on setting zones of distance protection relays.Optimal placement of TCSC to improve voltage stability limit considering impacts on setting zones of distance protection relays.
[50]TCCS: studied the impact of different firing control strategies on the SSR.Introduces a qualitative study on the effects of thyristor-controlled series capacitor (TCSC) on the distance protection strategies for PS and also suggests modifications in the conventional distance protection logic.
Table 4. The summary of the techniques implemented for frequency support.
Table 4. The summary of the techniques implemented for frequency support.
ReferenceTechnique/ProblemContribution
[81] Pitch angle controller and a rotor speed controller developed to. Assist in restoring grid frequency by adjusting its power output as required
[82,83]Examine random loading, nonlinearities, time delays, reduced inertia, and stochastic load fluctuations.This has been accomplished through the development of Multivector Model Predictive Power Control Consistently, the proposed algorithm has shown positive responses to these scenarios, effectively controlling the voltage and frequency
[84]Control parameters on frequency stability is determined using a root locus analysis This scheme can be used as a theoretical framework from which to choose control parameters
[85]Examine the impact of VSG settings on transient energy demand (TED) and maximum grid frequency deviation (MGFD) considering nonlinearities such as droop dead band.frequency management using the swing equation, which is based on droop control is developed
[86]Developed a coordination strategy for the virtual inertia control (VIC) and the frequency damping control (FDC).The developed strategy is performed using droop control-based swing equation.
[87]An adaptive droop frequency support (ADFS) scheme is developed inProposed approach employs an adaptive droop frequency support (ADFS) scheme at each receiving end converter (REC) to discern and distinguish the grid experiencing disturbance from the other unaffected grids within the multi-terminal HVDC (MTDC) system
[88]Assesses the capability of hydrogen electrolyzes to provide frequency control ancillary services, such as virtual inertia and primary and secondary frequency responseThis study adopts a modeling approach to formulate appropriate control strategies for both alkaline and proton exchange membrane technologies.
Table 5. Improvement of fault ride-through capabilities techniques.
Table 5. Improvement of fault ride-through capabilities techniques.
ReferenceMethod/Technique UsedContribution
[89]Current limitingA new control approach that uses both positive and negative sequences to regulate currents independently is proposed. Positive, negative, and zero sequence control (PNZSC) is employed simultaneously, and Improved. inverter-grid synchronization is achieved via the employment of a Dual Second Order Generalized Integrator-Frequency Locked Loop (DSOGI-FLL) in the presence of asymmetric faults. To lessen the effects of THD and power losses, an interleaved DC-DC converter and a Neutral Point Clamped (NPC) Inverter are employed.
[90]Bridge-type fault current limiter (FCL) with discharging resistorTo improve FRT at a low-cost series dynamic braking resistor (SDBR) has been adopted. There is no lag time in the operation of a dc reactor, which limits the fault current as it increases. Suppressing the immediate voltage drop is a useful property of the bridge-type FCL that can enhance the transient behavior of a wind energy conversion system (WECS).
[91]Hybrid genetic algorithm optimized Elman neural network controller It has been suggested that FRT capabilities could be enhanced by using a tailored dynamic voltage restorer controlled by a hybrid intelligent control method for proton exchange membrane fuel cell supported customized Dynamic Voltage Restorer (DVR)
[92]Novel DVR-based Odd-nary Cascaded Asymmetric Multi-Level Inverter (MLI)Compared to earlier types of MLIs, this new type introduces a staircase sinusoidal voltage with high level numbers against less switch numbers.
[93]Nonlinear back stepping control scheme Lyapunov functions (CLFs) are formulated as the negative definiteness or semi-definiteness of the derivatives of these CLFs at various stages of the controller design process.
[94]Fuzzy Logic Controlled Crowbar ProtectionThe crowbar switch’s timing and the WECS’s FRT capability have both been enhanced by the addition of a fuzzy logic controller (FLC) block
[95]Non-superconducting bridge-type fault current limiter (BFCL)Other research exploring and implementing low-cost BFCLs needs to fill the gap left by the lack of attention paid to proper impedance design as a potential option for fault impact mitigation in DFIG wind integrated HVDC systems. With BFCL-based fault current control, DFIG wind coupled VSC-HVDC systems can improve both their fault ride-through capabilities and transient stability.
Table 6. Improved protection schemes for RES integrated networks.
Table 6. Improved protection schemes for RES integrated networks.
ReferenceProblemSolution
[104]Diminished effectiveness seen in traditional distance protection systems on the tie line of DFIG-based wind farmsA novel R-L differential equation-based distance protection technique is proposed. This innovative method integrates faulted phase selection and considers the influence of high-frequency components, power system disturbances, and measurement errors.
[105] Implemented a time-domain distance relay using the R-L differential equation algorithm specifically designed for integration with wind power system.
[106]Enhance real power generation in changing environmental circumstancesAlgorithms are employed too. Both solar photovoltaic (SPV) and wind energy systems are controlled using the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm
Table 7. Techniques to improve accuracy and speed of distance protection.
Table 7. Techniques to improve accuracy and speed of distance protection.
MethodReferencesTechniques
Advanced Signal Processing Approach[134,135,136,137,138,139]
  • The Fourier transform (FT), the wavelet transforms (WT), and S-transform (ST).
Computational Intelligence Techniques[140,141,142,143]
  • Single Vector machine (SVM);
  • RF-based classifier;
  • DT-fuzzy logic.
Miscellaneous Approaches[99,119,139,144,145,146,147,148,149,150]
  • Online modification of impedance trajectory;
  • Synchro phasor-based impedance modeling;
  • Differential apparent power-based;
  • R-L DE-based protection scheme;
  • Superimposed component of current and voltage signal;
  • Real-time monitoring.
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Mazibuko, N.; Akindeji, K.T.; Moloi, K. A Review on the Impact of Transmission Line Compensation and RES Integration on Protection Schemes. Energies 2024, 17, 3433. https://doi.org/10.3390/en17143433

AMA Style

Mazibuko N, Akindeji KT, Moloi K. A Review on the Impact of Transmission Line Compensation and RES Integration on Protection Schemes. Energies. 2024; 17(14):3433. https://doi.org/10.3390/en17143433

Chicago/Turabian Style

Mazibuko, Ntombenhle, Kayode T. Akindeji, and Katleho Moloi. 2024. "A Review on the Impact of Transmission Line Compensation and RES Integration on Protection Schemes" Energies 17, no. 14: 3433. https://doi.org/10.3390/en17143433

APA Style

Mazibuko, N., Akindeji, K. T., & Moloi, K. (2024). A Review on the Impact of Transmission Line Compensation and RES Integration on Protection Schemes. Energies, 17(14), 3433. https://doi.org/10.3390/en17143433

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