Wideband Dynamic Monitoring and Control System for Power Systems with High Penetration of Renewable Energy and Power Electronics

Abstract

Wideband oscillation events, with frequencies ranging from several hertz to several kilohertz, have been frequently reported in modern power systems, posing significant challenges to grid stability and sustainability. In response, technologies for oscillation monitoring and analysis have received increasing attention. However, most existing technologies still rely primarily on traditional wide-area measurement systems, which struggle to meet the requirements for wideband oscillation monitoring. This paper first presents a comprehensive review of recent wideband oscillation events reported worldwide, highlighting their causes and adverse impacts on equipment security and system stability. Subsequently, a novel framework for a wideband dynamic monitoring and control system (WDMCS) is proposed, along with detailed descriptions of its principal components and key functions related to wideband oscillations. Finally, a demonstration of WDMCS has been developed, and its effectiveness has been validated through tests conducted on a hardware-in-the-loop platform. The potential and challenges of the proposed system in various domains of power system stability assessment and control are also discussed.

1. Introduction

Modern power systems are progressively evolving toward high penetration of renewable energy sources and power electronic devices, driven by the growing demand for low-carbon energy transitions and increasingly complex power supply requirements [1,2]. This transition is essential for achieving global sustainability goals, as it facilitates the integration of clean energy. In these power systems, however, traditional synchronous generators, converter-interfaced generations (CIGs) (e.g., wind turbines and photovoltaic units), flexible AC/DC transmission systems, and power electronics-based loads are interconnected through intricate electrical networks. Influenced by factors such as electromechanical dynamics, electromagnetic characteristics, and various control algorithms, these devices and systems may interact across multiple-time scales [3,4]. Such interactions may affect the dynamic behavior of power systems at various frequencies, potentially causing new stability issues. A representative phenomenon is the occurrence of wideband oscillations (WBOs), which refer to oscillations with frequencies ranging from several hertz to several kilohertz, caused by the interactions among power electronic devices and electrical networks [5,6,7]. In recent years, WBO events have frequently occurred worldwide, resulting in severe consequences such as power quality degradation, equipment damage, generator tripping, and even large-scale blackouts [8,9,10]. Clearly, WBOs pose a severe threat to the stability of power systems and efficient accommodation of renewable energy, thereby impeding progress toward sustainable energy systems. Therefore, the real-time monitoring, analysis, and control of wideband dynamics are essential to ensuring the safe, stable, and sustainable operation of modern power systems.

Recently, WBO analysis primarily relies on small-signal modeling and electromagnetic transient (EMT) simulations. Small-signal analysis involves linearizing the system around a specific operating point to derive state-space equations, which allows for a quantitative investigation of the oscillation mechanisms and influencing factors [11,12]. EMT simulations, on the other hand, construct detailed models based on topology and control algorithms, and numerically solve the associated differential-algebraic equations [13,14]. However, the operational conditions of power systems exhibit significant variability due to the inherent randomness and intermittency of renewable energy sources, as well as the nonlinear and uncertain behavior of power electronic devices. Moreover, WBOs are characterized by multiple oscillation modes, time-varying amplitudes and frequencies, and wide-area propagations [15]. As a result, conventional analytical methods face inherent limitations in modeling all operating scenarios and conducting real-time analysis.

It is well-known that wide-area measurement systems (WAMS) use synchrophasors from phasor measurement units (PMUs) to achieve dynamic monitoring, analysis, and control of large-scale power systems [16]. PMUs were originally designed to measure fundamental phasors, while components with non-fundamental frequencies are typically regarded as interference or noise. To avoid aliasing effects that could compromise the accuracy of fundamental phasor measurements, these non-fundamental components are often intentionally filtered out. As a result, conventional PMUs primarily monitor phasor information near the fundamental frequency (e.g., 50 Hz or 60 Hz), thereby failing to capture wideband dynamics in modern power systems. Moreover, after PMUs transmit data to the WAMS center at a rate of 50 Hz or lower, the information associated with wideband dynamics becomes difficult to reconstruct accurately due to signal aliasing. Some studies have attempted to extract subsynchronous oscillation (SSO) information from the phasor data received by WAMS [17]. However, the low reporting rate and the lack of detailed spectral content significantly limit the observability of wideband dynamics. Consequently, existing WAMS functionalities are primarily focused on operational characteristic analysis, low-frequency oscillation (LFO) analysis, and transient stability control of power systems [18], but are not capable of effectively monitoring, analyzing, or controlling WBOs. This gap highlights a critical challenge in maintaining grid sustainability with high penetration of renewable energy and power electronics.

The main contributions of this paper are as follows:

• This paper systematically analyzes the key characteristics of WBOs and their impacts on equipment security and system stability, based on WBO events reported globally in recent years. It further highlights the inherent limitations of conventional monitoring systems in real-time analysis of wideband dynamics. The findings emphasize the importance of wideband monitoring for sustainable power system operation.
• A novel framework of the wideband dynamic monitoring and control system (WDMCS) is proposed, with a detailed description of its core components: wideband dynamic measurement and control unit (WDMCU), wideband dynamic data concentrator (WDDC), and the wideband dynamic analysis and control center (WDACC).
• A demonstration of WDMCS has been developed, and its effectiveness has been validated through hardware-in-the-loop (HIL) testing. In addition, based on the real-world application experience in China, this paper discusses the potential of the proposed WDMCS for broader deployment in future power systems. The practical implications of this system support the long-term sustainability of energy infrastructure.

The rest of the paper is organized as follows. Section 2 discusses WBO events reported worldwide. Section 3 presents the framework of the WDMCS along with its key functions. Section 4 discusses the development of the demonstration of WDMCS and explores its applications and future research directions. Section 5 concludes this paper.

2. Characteristics and Consequences of Wideband Oscillations

With the development of large-scale interconnected power systems, various non-fundamental frequency oscillations have emerged. Early examples of these include LFOs caused by synchronous generator interactions [19], and subsynchronous resonances primarily involving turbine-generators [20]. These oscillations have been extensively studied, and mitigation strategies are well-established [21,22,23]. However, since the electromagnetic oscillation incidents of wind farms in Minnesota (2007) and Texas (2009) [8], WBO events have been increasingly reported, particularly in systems equipped with CIGs and flexible AC/DC transmission infrastructures, as shown in Figure 1. Unlike traditional electromechanical oscillations, WBOs exhibit distinct characteristics. Based on their frequency, WBOs can be classified into several types, including low-frequency oscillations [24,25], sub-/super-synchronous oscillations [26,27,28], near-fundamental-frequency oscillations [29], and medium- and high-frequency oscillations [10,30]. These oscillations typically arise from interactions among diverse equipment and systems, such as doubly fed induction generators (DFIGs) with series-compensated lines [14], voltage source converters (VSCs) with AC grids [31,32], and flexible DC converters with AC grids [33,34].

Despite their diversity, WBOs share key characteristics, the most prominent of which include:

• WBOs are associated with power electronics control, exhibiting a broad frequency range, multiple dominant modes, and frequency coupling effects. For example, the Zhangbei flexible DC grid in China (four ±500 kV converter stations, 4.5 GW total capacity and over 2 GW renewable integration) experienced several WBO incidents between 2020 and 2022. These included SSOs (2–5 Hz) due to interactions between the Mijiagou wind farm and the DC system, high-frequency oscillations at the Zhongdu converter station (1000 Hz) and the Kangbanuoer converter station (700–1500 Hz), and near-fundamental-frequency oscillations (44/56 Hz) at Kangbanuoer.
• WBO characteristics are highly sensitive to the operational conditions of power systems, resulting in significant time-varying oscillation frequency. For example, during a 2022 event at the Yangjiang offshore wind farm in Guangdong, China (planned capacity of 2 GW), oscillation frequencies fluctuated between 977 Hz and 1328 Hz within three minutes. Oscillation current amplitudes exceeded the fundamental, leading to drop-out of wind turbines.

As evidenced by past events, WBOs pose serious threats to the equipment security and system stability, including:

• Equipment damage: WBOs induce mechanical and electrical stresses that accelerate aging and failure of critical equipment such as transformers and power electronic converters. For example, chopper resistors of several DFIGs were damaged during the oscillation event in Zhangbei system [35], as shown in Figure 2a. Similarly, the Texas event damaged crowbar circuits and DC bus capacitors in wind turbines [36]. Oscillations in a wind farm in the North Sea, Germany, caused severe failures in filter capacitors and transformers [30].
• Equipment disconnections: WBOs can trip renewable generation units and power electronic devices. For example, an event in Guyuan, China, led to the disconnection of thousands of wind turbines [37], as shown in Figure 2c. A similar event in the Yunnan DC transmission project resulted in the shutdown of a static synchronous compensator (STATCOM) [38]. In Victoria, Australia, oscillations led to the tripping of multiple wind farms [39].
• Widespread system stability issues: WBOs can propagate through transmission networks, endangering distant power stations. In Hami, Xinjiang, China, SSOs originating from a wind farm caused torsional vibrations and forced the shutdown of thermal power plants hundreds of kilometers away, resulting in a significant frequency drop [15]. Likewise, the Hornsea offshore wind farm tripped due to oscillations, contributing to a major blackout in the United Kingdom [40].

In summary, WBOs significantly endanger equipment security and system stability. Effectively addressing this challenge requires a reliable system capable of wide-area monitoring, analysis, and control of wideband dynamics. Such a system must be able to simultaneously detect multiple oscillation modes and adapt to time-varying frequencies. In addition, it should support real-time data acquisition and analytics to facilitate timely operator interventions and issue early warnings. Ultimately, the objective is to proactively suppress WBOs or activate emergency protection mechanisms when necessary.

3. The Wideband Dynamic Monitoring and Control System

The proposed WDMCS shares architectural similarities with conventional WAMS, as depicted in Figure 3. The system comprises three principal components: WDMCUs, WDDCs, and WDACC. WDMCUs and WDDCs are typically deployed at renewable energy power plants and substations to facilitate wideband dynamic data monitoring, integration, and localized control. All WDMCUs and WDDCs are time-synchronized via the China’s BeiDou navigation satellite system (BDS) or the global positioning system (GPS). The WDACC can be incorporated into the operational control system of power systems via a loosely coupled architecture, thereby minimizing disruptions to existing monitoring systems and reducing integration overhead. Alternatively, the WDACC may function as a standalone platform, offering enhanced flexibility, compatibility, and scalability to meet evolving operational demands.

Compared with the existing WAMS, which primarily collect and utilize fundamental synchrophasor measurements, the proposed WDMCS significantly extends functionality to monitor, analyze, and mitigate oscillations across a broad frequency range from several Hz to several kHz. Typical applications related to power system operation include oscillation visualization, oscillation early warning, oscillation source identification and ranking, and oscillation mitigation and protection. These functionalities represent a substantial advancement over conventional WAMS, which are limited to fundamental frequency phenomena and low-frequency oscillations. The WDMCS should therefore be regarded as an advanced and expanded version of the WAMS, specifically designed to address the dynamic challenges of modern power systems with high penetration of power electronics and renewables. The main functions of each component are described below:

3.1. Wideband Dynamic Measurement and Control Unit

The WDMCU serves as a critical edge device that supports the operation of the WDMCS. It continuously acquires multi-channel voltage and current measurements from renewable energy power plants and substation transmission lines [5]. These measurements form the foundation for wide-area dynamic detection, analysis, and mitigation. The WDMCU should integrate existing PMU functionalities to comply with the IEEE C37.118.1-2011 standard [41] for synchronized measurements and meet current engineering application requirements. In our laboratory prototype, the algorithm for fundamental frequency components (45–55 Hz) employs a traditional PMU measurement approach [42]. Type testing has verified that the device satisfies the IEEE standard requirements for synchronization accuracy. Beyond standard PMU capabilities, the WDMCU is specifically engineered for wideband dynamic analysis. Its advanced signal processing pipeline extends to harmonic and inter-harmonic components, enabling the precise characterization of oscillatory phenomena across a broad frequency range.

The main steps for wideband measurement in the WDMCU are outlined below:

• Synchronized Data Acquisition: Multi-channel voltage and current signals are simultaneously sampled at a high rate (e.g., 12.8 kHz) using a GPS-synchronized clock, ensuring precise time alignment across all measurement points as mandated by [41].
• Wideband Phasor Measurement and Oscillation Assessment: Based on the acquired waveforms, the fundamental phasor as well as harmonic and inter-harmonic phasors across a wide frequency range are calculated. For example, an interpolated DFT algorithm can be employed to mitigate spectral leakage and the picket-fence effect while maintaining relatively low computational complexity. The calculation process can be represented as [43]:

  $$X(m)={\displaystyle \sum _{n=0}^{N_w-1}w(n)s(n)e^{-j2\pi \frac{n}{N_{\mathrm{w}}}\cdot m}}$$

  (1)

  $${\hat{a}}_k=\left|X(m_k)\right|{\displaystyle \frac{2\pi \delta }{N_w\sin (\pi \delta )}}(1-{\delta }^2)$$

  (2)

  $${\hat{f}}_k=(m_k+\delta )f_s/N_{\mathrm{w}}$$

  (3)

  $${\hat{\varphi }}_k=\angle X(m_k)-\pi \delta$$

  (4)

  where s(n) is the sampled signal; w(n) is the window function; X(m) is the represents the DFT result; N_w is the length of the observation window; m_k is the index of the local peak spectral line related to the kth oscillation component; ${\hat{a}}_k$, ${\hat{f}}_k$ and ${\hat{\varphi }}_k$ represent the corrected amplitude, frequency, and phase, respectively. δ is the compensation parameters which can be calculated as:

  $$\delta ={\displaystyle \frac{2\left|X(m_k+1)\right|-2\left|X(m_k-1)\right|}{\left|X(m_k-1)\right|+2\left|X(m_k)\right|+\left|X(m_k+1)\right|}}$$

  (5)
  Furthermore, dynamic indices related to oscillation modes can be calculated, such as the oscillation damping ratio and oscillation power flow. Upon detection of an oscillation, the unit immediately triggers waveform recording and storage for post-event analysis which is similar to the fault recording function in protection relays.
• Data Packaging and Transmission: The computed time-aligned phasor data and oscillation metrics are packaged into frames compliant with standard communication protocols and transmitted uplink to the WDDC or directly to the WDACC for system-wide situational awareness.

In addition to its monitoring role, the WDMCU acts as an intelligent control agent. It can execute either pre-programmed local logic or execute control commands received from the WDACC. These commands, which may include modulating converter setpoints or triggering breaker operations, are issued to grid assets to actively dampen wideband oscillations and maintain system stability.

3.2. Wideband Dynamic Data Concentrator

Functioning as a data aggregation and communication intermediary, the WDDC collects synchrophasor data from multiple geographically proximate WDMCUs. It aligns all phasor measurements temporally with the high-precision timestamps (±1 µs accuracy, compliant with IEEE C37.118.1) provided by each WDMCU via the BDS/GPS. And the WDDC performs data backup before forwarding the consolidated information to the WDACC. Key roles of the WDDC include:

• Enhancing overall communication efficiency within the monitoring architecture.
• Ensuring data integrity and continuity during communication failures.
• Distributing localized control or emergency protection commands to renewable energy plants or substations, facilitating rapid, regional response to WBO events.

3.3. Wideband Dynamic Analysis and Control Center

The WDACC functions as both a centralized data repository and an advanced computational platform for the online and offline analysis of wideband dynamics. Its functionalities are broadly classified into two categories: basic data processing and advanced analytics.

The basic processing functions consist of aggregating the monitoring data from WDMCUs and WDDCs, synchronizing the data with timestamps, and storing the structured data in both real-time and historical databases for efficient retrieval and subsequent analysis.

The advanced analytical functions leverage wideband dynamic monitoring data to enable comprehensive analysis and control of various stability phenomena in power systems. These encompass not only conventional functions commonly addressed in the WAMS, such as LFO analysis, transient stability control, and voltage/frequency monitoring and control [2], but also the assessment and mitigation of novel stability issues in modern power systems. Specifically, the functions related to WBOs can be categorized according to different stages of power system operation: (1) wideband state estimation, early warning, and source identification during oscillation onset; (2) adaptive mitigation and emergency protection following oscillation instability or system failure.

Several representative functionalities related to WBOs will be discussed as follows:

3.3.1. Wideband State Estimation and Panoramic Visualization

Wideband state estimation is an extension of traditional state estimation techniques, specifically designed to process synchronized phasor measurements of voltage and current associated with the same oscillation mode, collected from multiple WDMCUs. Its objective is to reconstruct the real-time dynamic state of the power system under oscillatory conditions [44,45]. Essentially, the modal phasors of all bus voltages and line currents are treated as state vectors to be estimated, whereas the actual measured phasor data serve as observational inputs for the estimation process. Wideband state estimation plays a critical role in enhancing the accuracy and reliability of monitoring data by correcting or eliminating bad data [5]. As such, it provides the foundational support for all advanced applications. For example the relationship between the voltage/current phasor measurements and the system state at the oscillation frequency f can be expressed as [44,45]:

$$Q_f=H_f{U}_f$$

(6)

where Q_f is a column vector containing the voltage and current phasor measurements at the oscillation frequency f; H_f is the coefficient matrix; and U_f is a column vector that contains the voltage state of nodes of the grid.

The voltage state can be estimated based on the weighted least square method, which is given by:

$$U_f={(H_f^{*}{W}^{-1}{H}_f)}^{-1}{H}_f^{*}{W}^{-1}{Q}_f$$

(7)

where W is a weight matrix that is pre-set based on the measurement errors.

When the oscillation voltages of all the nodes are estimated, the oscillation currents through all branches can also be obtained. Then, the WDACC can provide a comprehensive view of the wideband oscillations across the power grid, enabling dispatchers to make more informed decisions and perform deeper diagnostic analyses. It supports a panoramic visualization of oscillation behavior within the actual power network. The specific procedure for this function is outlined as follows. First, the topological diagram of the target power grid is constructed. Then, the results from the wideband state estimation (such as modal voltages and currents) are annotated according to a consistent reference direction. This process generates a wideband oscillation state-distribution map, which intuitively reflects the spatial distribution and propagation characteristics of oscillation modes within the system.

3.3.2. Early Warning of Wideband Oscillations

The WDACC can assess whether the power system is experiencing WBOs by analyzing modal phasors and predefined safety criteria. When necessary, it promptly issues alerts to dispatchers, enabling timely implementation of control and protection measures to mitigate oscillation impacts on the grid.

For example, a representative metric for evaluating oscillation risk is the damping coefficient, which reflects the dynamic behavior of oscillatory modes [44]. This coefficient can be estimated using voltage and current phasor measurements, thereby supporting real-time risk assessment of WBOs. A wideband oscillation is deemed unstable or high-risk if the average damping ratio is less than zero (or negative) and the maximum phasor amplitude (voltage or current) exceeds a predefined threshold. This approach relies solely on real-time monitoring data and is independent of equipment models or network topology, making it especially well-suited for online early-warning applications.

Additionally, the WDACC can estimate impedance models of power devices based on monitoring data, and combine these with grid topology and line parameters to construct a comprehensive impedance network model [46]. This model enables further stability analysis of WBOs from a system-level perspective. Beyond identifying oscillation sources, it also supports emergency control decision-making by evaluating the influence of individual devices on system stability.

3.3.3. Identification of Wideband Oscillation Sources

A wideband oscillation source is defined as an electrical device that injects oscillatory energy into the power system. This function aims to identify such sources and provide a foundation for subsequent mitigation and protection. Since oscillation sources typically supply energy into the grid, the modal power absorbed by various devices can be used to identify these sources [45]. The modal power can be calculated as follows:

$$P_f=\mathrm{Re}\left\{{\displaystyle \sum _{k=0,1,2}{\dot{U}}_{fk}{\dot{I}}_{fk}^{*}}\right\}$$

(8)

where P_f is the modal power at the oscillation frequency f; k = 0, 1, 2 denote three phases A, B and C; ${\dot{U}}_{fk}$ and ${\dot{I}}_{fk}$ are measured voltage and current phasors at the oscillation frequency f, respectively.

For single-port devices, such as generators and energy storage units, the absorbed modal power is represented by the power obtained from the terminal voltage and current phasors of the device. For multi-port devices, such as transmission lines and transformers, the absorbed modal power is computed as the sum of the powers across all ports. A device is identified as an oscillation source for a specific mode if it absorbs negative active modal power. In recent years, various methods for estimating oscillatory power or dissipative energy flow (DEF) have been proposed to identify the oscillation source accurately under different conditions [47,48,49]. Although these metrics differ in formulation, all can be derived from wideband phasor measurements, making oscillation source identification feasible within the WDACC. Additionally, the excitation effect of a device can also be assessed using its equivalent impedance [45]. A positive equivalent resistance implies that the device is absorbing active modal power, whereas a negative resistance suggests an excitation effect for oscillations.

After identifying the source for each oscillation mode, the spatial distribution of oscillation sources and their corresponding power can also be overlaid onto the panoramic system diagram, offering dispatchers an intuitive visualization of oscillation propagation paths throughout the grid.

3.3.4. Wideband Oscillation Mitigation and Protection

The WDACC can make emergency control decisions based on the results of oscillation source identification and system stability analysis. It can determine the specific locations and quantities of devices requiring emergency tripping in real time. Following this analysis, the WDACC issues control commands to distribute protection relays throughout the grid, instructing them to execute protective actions essential for maintaining system stability. On one hand, using locally monitored data, the WDACC can dispatch control commands to regulated devices (e.g., renewable energy generation units) to establish localized closed-loop control mechanisms. This approach effectively isolates wideband oscillation sources and reinforces the dynamic stability of the power system. On the other hand, the WDACC can integrate the remote and local monitoring information, enabling wide-area coordinated control and emergency protection.

Furthermore, the control decision-making framework of the WDMCS is designed with extensibility and compatibility as core principles, allowing it to adapt to diverse control methodologies and solutions across various power system scenarios. For example, the system can be extended to address stability challenges in DC grids, such as commutation failure in LCC-HVDC links or DC voltage oscillations in VSC-HVDC systems [50], by incorporating appropriate stability indices and tailored control algorithms into the WDACC’s analysis and command logic.

Research and practical applications of local monitoring-based oscillation mitigation and protection are relatively mature. For example, in [51], WDMCUs are deployed at all wind farms, where they measure voltage and current phasors associated with specific oscillation modes to compute each wind farm’s impedance at the oscillation frequency. Using the topology of the power system, the aggregated impedance of the system is calculated, and the stability of the oscillation mode is subsequently evaluated. If the analysis indicates an unstable oscillation mode, wind turbine generators (WTGs) are tripped to suppress oscillations based on a sensitivity index. Once the optimal tripping strategy is determined, commands are transmitted from the main station to all relevant wind farms via communication links, prompting the execution of protective tripping operations. Additionally, a similar framework has been applied to suppress HFOs in modular multilevel converter-based high-voltage direct current (MMC-HVDC) transmission systems [52].

To ensure the reliable integration of the proposed WDMCS into practical grid environments, several critical aspects of control coordination and operational safety must be addressed. First, command conflicts with existing protection schemes are mitigated through a hierarchical coordination strategy. In this approach, traditional fault-based protections remain the highest priority, while WBO control functions as a supplementary stability-oriented layer with pre-defined logic interlocks. Second, although a formal safety integrity level (SIL) assessment is beyond the scope of this research prototype, the system architecture incorporates design principles from functional safety standards (e.g., IEC 61508 [53]) to enhance reliability. Finally, a clear functional role division is established between the transmission system operator (TSO) and equipment manufacturers (OEMs): the TSO retains overriding authority over control decisions and system settings, whereas OEMs are responsible for providing and maintaining the hardware and software platforms without operational control privileges. These design considerations are essential for the future practical deployment of the WDMCS in large-scale power grids.

4. Development and Tests of a Demonstration of WDMCS

4.1. Implementation of a Demonstration of WDMCS

A demonstration of WDMCS, consisting of two WDMCUs, one WDDC, and one WDACC server, has been successfully developed in our laboratory, as shown in Figure 4. Each WDMCU incorporates a field-programmable gate array (FPGA) for acquiring up to eight sets of three-phase voltage and current signals. Phasors within the 3–2500 Hz range are computed using digital signal processors (DSPs) and sorted by amplitude. After time-stamping and packaging, the monitoring data are transmitted to the WDDC or WDACC at a rate of 100 frames per second. The WDDC facilitates data exchange with the WDMCU and WDACC via optical fiber. The WDACC, implemented on a Linux platform, integrates data storage and analysis functionalities, including wideband state estimation, oscillation source identification, early warning, panoramic visualization, and oscillation protection.

To support the transmission of diverse data types within the WDMCS, we have extended existing communication protocol for WAMS, such as the IEEE standard C37.118.2-2024 [54] and the Chinese national standard GB/T 26865.2-2011 [55]. It has the following improvements:

• Incorporation of phasor data for harmonics and inter-harmonics, organized in descending order of amplitude.
• Modification of configuration frames to accommodate variable numbers of oscillation modes.
• Addition of command frames, enabling the WDACC to issue control and protection commands to WDMCUs.
• Support for communication among WDMCUs, WDDC, and WDACC.

4.2. The HIL Test Platform

A HIL test platform has been developed with a real-time digital simulator (RTDS) to validate the feasibility of the WDMCS. As illustrated in Figure 4, the platform comprises an RTDS unit, several power amplifiers, and the demonstration of WDMCS. A simulation model of an actual power system with renewable generation transmitted via HVDC lines was constructed in RTDS. This model includes four photovoltaic power plants (PV#1−PV#4) and one wind power plant (WT#1), as shown in Figure 5. To simulate the deployment of WDMCUs, three-phase voltage and current signals at the ports of all renewable power plants were output from the RTDS. These signals were subsequently amplified by power amplifiers and then fed into the WDMCUs. Monitoring data were transmitted via optical fibers to the WDDC and WDACC. Synchronization among WDMCUs, WDDC, and WDACC was achieved with the BDS.

4.3. Test Results

In the initial state of the simulation model, PV#1–PV#4 were operating with a 500 W/m² irradiance, while WT#1 was offline. When the irradiance of PV#4 was abruptly increased to 600 W/m², oscillations appeared in the system. This phenomenon occurs because the rapid increase in power injection from PV#4 disturbs the power balance of the system, altering the output impedance characteristics of its grid-tied inverter and inducing negative damping effects that excite specific oscillatory modes within the network. Sustained oscillations were observed in both the voltage at the point of common coupling (PCC) and the current of the grid-connection lines for all renewable energy plants. As a representative example, Figure 6 presents the current waveform of the PV#1 grid-connection line, as acquired by the WDMCU, along with the measured oscillation frequencies, amplitudes, and phase angles of the Phase A current. To mitigate the influence of noise, the WDMCU identified oscillation modes with amplitudes exceeding 2% of the fundamental component. Two such modes (38.8/61.2 Hz) were detected at 60 ms and 70 ms after the change in irradiance at PV#4, respectively. These results were transmitted via the WDDC to the WDACC.

The WDACC integrated and analyzed the data collected from all WDMCUs and the WDDC. As shown in Figure 7, it detected divergent oscillatory behavior based on damping coefficients and issued a system warning at 80 ms after the change in the irradiance. Utilizing the DEF-based oscillation source identification method in [44], the WDACC subsequently quantified the oscillation power contributions from each renewable power plant. Figure 8 shows that both PV#1 and PV#4 continuously injected oscillation power into the grid for mode #1, which were identified as oscillation sources according to the principles of the DEF method. Notably, PV#4 was identified as the dominant source due to its substantially larger oscillation power injection. Consequently, the WDACC recommended emergency tripping of PV#4 to mitigate the oscillation and prevent further system deterioration.

Notably, during the operation of the demonstration of WDMCS, the WDACC could display oscillation data from any WDMCU in real time and supports post-event playback for detailed analysis. This HIL test provides preliminary validation of the proposed WDMCS’s effectiveness in wideband dynamic monitoring, data transmission, and system analysis. Looking ahead, to further comprehensively evaluate the system’s robustness and scalability, it is necessary to expand the test scenarios to include a wider set of edge cases and potential failure modes, thereby validating the performance of the WDMCS under more diverse and challenging grid conditions.

4.4. Applications and Challenges

The WDMCS has been successfully implemented in several demonstration projects across China, such as the Yunnan Power Grid, Eastern Inner Mongolia Grid, and the laboratory of the China Southern Power Grid, demonstrating promising results. In the Yunnan Power Grid, the developed WDMCS has been configured to monitor dynamic phenomena within the frequency range of 2.5–100 Hz. Substations including Zixi, Lufeng, Heping, Lucheng, and Huangping have been equipped with WDMCUs, which continuously measure both fundamental-frequency phasors and sub-/super-synchronous phasors. A WDACC server has also been established at the Yunnan grid dispatch center, which integrates functions such as wideband state estimation, panoramic visualization, oscillation source identification, and early warning. Moreover, it supports retrospective analysis of historical oscillation events to reveal underlying oscillatory characteristics. During field tests, the system successfully detected sub/super-synchronous oscillations at 25/75 Hz within the grid. It issued early warnings based on modal current amplitude and damping ratio indicators and accurately identified the oscillation source near the Zixi Substation. These outcomes confirm the practical feasibility and effectiveness of the proposed WDMCS in real-world power system applications.

Looking ahead, the WDMCS is expected to play a pivotal role in the modernization and digitalization of power systems. However, there still exist many challenges on these issues, but they have not been fully addressed in existing research. The following sections will discuss the practical potential, ongoing challenges, and promising future research directions pertaining to its application.

4.4.1. Multi-Dimensional Stability Assessment of Power Systems

By integrating both classical and modern theories of power system dynamics, the WDMCS enables a comprehensive and multi-layered stability evaluation framework. By leveraging real-time, high-resolution phasors, the system can concurrently assess multiple dimensions of stability (e.g., synchronism, voltage, frequency, and oscillation) across diverse spatial and temporal scales. The tiered architecture supports granular diagnostics at the equipment level (e.g., converters, STATCOMs, and energy storage systems) while aggregating this information to evaluate system-wide stability margins. This facilitates a shift from traditional post-fault analysis to predictive and proactive stability management, allowing system operators to identify and mitigate emerging risks before they escalate into critical failures.

4.4.2. Synchro-Waveform Data Analytics and Transmission

With the advancement of data compression, edge computing, and time-synchronized sampling technologies, the WDMCS is expected to evolve into a hybrid monitoring platform that supports both phasor data and synchro-waveform data. However, integrating high-resolution waveform data across hundreds of units introduces significant challenges in communication latency, bandwidth, and packet loss. To ensure scalability and real-time performance, future research will focus on adaptive and intelligent data management strategies, including:

• Developing adaptive transmission protocols that switch between minimal essential data reporting during steady-state conditions and high-resolution waveform streaming only during triggered oscillatory or fault events.
• Leveraging compressed sensing and sparse signal recovery techniques to reduce communication bandwidth while retaining critical dynamic information.
• Implementing communication-aware data prioritization, potentially over modern telecom infrastructures (e.g., MPLS-TE, OTN, or 5G network slicing), to ensure low-latency and high-reliability transmission for critical monitoring and control data.

Such advancements will significantly enhance situational awareness, dynamic behavior monitoring, and fault diagnosis in modern power systems, enabling more efficient and responsive wide-area monitoring.

4.4.3. Wide-Area Coordinated Control and Protection

The WDMCS provides a unified framework for wide-area coordination, utilizing system-wide real-time data to enable intelligent and adaptive control strategies. Future enhancements will emphasize reducing end-to-end response time and improving control agility, particularly through:

• Fast and Accurate Measurement Techniques: Embedding advanced algorithms (e.g., predictor-based or iterative methods) in WDMCUs to enable rapid and precise estimation of oscillation parameters.
• Pre-Defined and Localized Control Strategies: Identifying high-risk oscillation scenarios through offline studies and embedding pre-calculated control actions into regional WDDCs or even local WDMCUs. These strategies can be triggered autonomously based on real-time measurements, significantly shortening control latency.
• Hybrid control architectures: Integrating fast local control loops with centralized supervisory optimization to balance responsiveness with global coordination. This approach ensures that critical oscillations are suppressed promptly while maintaining overall system stability.

Such capabilities are essential for future systems with high penetration of renewables, multi-infeed power electronics, and rapidly changing dynamic characteristics.

4.4.4. Scalability and Communication Performance in Large-Scale Deployment

The practical deployment of the WDMCS across hundreds of monitoring and control units requires careful attention to communication network performance. While existing telecommunications infrastructures (e.g., MPLS, OTN) provide high reliability and low latency for dedicated channels, large-scale implementations may still encounter challenges in end-to-end latency accumulation and potential packet loss. To address these concerns, the system architecture incorporates several key design principles:

• Hierarchical Data Management: The tiered structure (WDMCU-WDDC-WDACC) enables distributed data processing and aggregation, reducing the burden on core communication links and minimizing latency in data consolidation.
• Adaptive Reporting Mechanisms: As mentioned before, the system employs state-dependent data transmission, drastically reducing network load during normal operation while ensuring high-resolution data availability during critical events.
• Modern Telecom Integration: Future work will explore integration with emerging technologies such as 5G network slicing, which can provide dedicated ultra-reliable low-latency communication channels for grid control applications.

These design considerations, combined with ongoing research in adaptive networking and distributed intelligence, ensure that the WDMCS remains scalable, responsive, and practical for deployment in extensive power networks with hundreds to thousands of units.

4.4.5. Techno-Economic Analysis and Commercial Viability

A comprehensive assessment of the capital expenditure (CAPEX) and operational expenditure (OPEX) for large-scale WDMCS deployment represents an essential direction for future research. While the current study focuses on architectural design and functional validation, the economic feasibility compared to conventional PMU/µPMU deployments requires detailed investigation. Key factors influencing cost structures include:

• Hardware integration costs for WDMCUs incorporating wideband measurement and local analytics capabilities;
• Software licensing models for the WDACC platform, considering its extensibility to incorporate diverse wideband dynamic analysis and control functionalities;
• Deployment expenses related to communication infrastructure upgrades and system integration with existing utility systems.

Future work will involve developing detailed cost models and business cases to evaluate the economic viability of WDMCS deployment across different grid scenarios.

5. Conclusions

With the increasing penetration of renewable energy and power electronics, the wideband dynamic characteristics of modern power systems have been significantly affected, leading to the frequent occurrence of WBOs. This paper analyzes globally reported WBO events in recent years and proposes a WDMCS for modern power systems. The main conclusions are as follows:

• WBOs present a serious threat to the stability and security of power systems. These oscillations are highly sensitive to power electronic control and system operating conditions, and exhibit distinct characteristics such as a broad frequency range, multiple dominant modes, and time-varying amplitudes and frequencies. Existing monitoring systems are insufficient for monitoring and analyzing such complex dynamic behaviors in real time, which is critical to supporting the transition to sustainable power systems.
• The proposed WDMCS enables real-time monitoring of wideband voltage and current phasors across the power grid. By integrating this data, the system facilitates wideband state estimation, panoramic visualization, early warning and source identification of oscillations, as well as adaptive mitigation and emergency protection. These functions enhance the reliability and resilience of power systems with high renewable penetration, thereby supporting operational sustainability.
• The demonstration of WDMCS and its real-world applications in China have validated its feasibility and effectiveness. Moreover, the proposed WDMCS shows promising potential in multiple areas, such as multi-dimensional stability assessment, synchronized waveform and wide-area coordinated control. However, further research is needed to address challenges related to advanced data transmission and compression techniques, scalability for large-scale grid deployment, and commercial viability.