Search Results (176)

The intermittent nature of photovoltaic (PV) energy, especially under nonlinear and unbalanced loading situations, has made it more difficult to ensure steady operation as it is increasingly integrated into modern power systems. The Power Quality (PQ) issues cause severe degradation of both system performance and device lifetime. A novel Neural Power Quality Network (NeuPQ-Net) controlled Unified Power Quality Conditioner (UPQC) combined with a Quasi Z-Source Lift (QZSL) converter for PV applications is presented in this research as a novel solution for addressing these issues. The QZSL converter, which is controlled by a Maximum Power Point Tracking (MPPT) algorithm based on Perturb and Observe (P&O), increases the PV source voltage to the necessary DC-link level. A Zebra Optimisation Algorithm tuned PI (ZOA-PI) controller continually adjusts PI gains for quick and accurate regulation, ensuring steady DC-link voltage. Unlike conventional Synchronous Reference Frame (SRF) or Decoupled Double Synchronous Reference Frame (DDSRF)-based reference generation, the proposed NeuPQ-Net operates directly in the abc domain, eliminating Phase-Locked Loop (PLL) dependency and reducing computational complexity. Simulation and hardware prototype validations demonstrate that the proposed system achieves significant improvements in PQ indices, including reduced Total Harmonic Distortion (THD), faster response to transients, and enhanced voltage regulation, while complying with IEEE-519 standards. Full article

Monitoring power quality (PQ) and classifying disturbances are essential for guaranteeing the reliable operation of contemporary electrical systems. Nonetheless, deriving discriminative features from PQ signals poses difficulties due to the complexity and non-stationary characteristics of disturbances. Therefore, this research introduces a novel Hybrid Adaptive Multi-Resolution Feature Extraction (HAMRFE) approach for classifying power quality disturbances (PQDs). The proposed HAMRFE framework incorporates six synergistic techniques: adaptive signal decomposition, multi-resolution wavelet analysis, time–frequency analysis, morphological feature extraction, entropy-based feature extraction, and feature selection optimization. Experiments are performed on a dataset consisting of fifteen types of PQDs with differing noise levels. In addition, the performance of five classification algorithms is assessed, including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Extreme Gradient Boosting, and K-nearest neighbor. The results indicate the exceptional efficacy of SVM utilizing HAMRFE features, with classification accuracies of 99.86% for noiseless signals, 99.85% at 40 dB, 99.82% at 30 dB, 99.74% at 20 dB, and 97.92% at 10 dB noise levels. Additionally, an analysis of different feature set sizes reveals that the set comprising 125 features is optimal at all noise levels, achieving a balance between computational efficiency and classification accuracy. Finally, the proposed HAMRFE approach exhibits remarkable resilience to noise and offers a thorough framework for classifying PQDs in practical applications. Full article

The rapid deployment of electric vehicles (EVs) has introduced new challenges to distribution networks, which are mainly related to power quality and grid reliability. Electric vehicle chargers behave as nonlinear loads because they are based on power electronic converters, which generate harmonic currents, cause voltage distortion, increase stress on network components, and might impact the overall power quality of distribution networks. In this study, power quality (PQ) measurements and harmonic characteristics were investigated for five electric vehicle models, namely the BYD Song Plus, Volkswagen ID6, Neta U, Nissan LEAF 2016, and Tesla Model 3. Measurements were carried out for different power levels—slow AC, low-power and fast AC, high-power charging modes—to evaluate the PQ characteristics and harmonic behavior of EVs. Fast charging power levels for most vehicles ranged between 5 and 11 kW, while slow charging ranged between 2.7 and 3.6 kW. It is found that harmonic characteristics, total harmonic current distortion (THD_I), and harmonic distribution depend on the EV type and the charging mode. This study found that THD_I varies between 1.5% and 10.72% for the tested EVs. Comparison with IEC power quality standards indicates that the impact of electric vehicle charging on voltage quality is limited, while current harmonic distortion varies significantly among vehicle models. Harmonic analysis reveals that the third and fifth orders dominate across most of the tested EVs, while the transition from slow to fast charging power level generally reduces low-order harmonics in most models, with vehicle-specific redistribution patterns that reflect converter topology and control strategy. The results also show that some EV chargers draw reactive power and operate with a lagging power factor, whereas other vehicles inject reactive power and operate under leading power factor conditions. Full article

Induction motors (IMs) are the largest consumers of electrical energy across most industrial sectors owing to their widespread application. The power losses in IMs significantly depend on the quality of the supply voltage. The presence of various power quality (PQ) disturbances, such as voltage deviation, voltage unbalance, and voltage harmonics, may increase the power losses by over 60%, even if the PQ fulfils the standards. To ensure the energy-efficient operation of IMs, PQ standards should be modified. One possible solution is the implementation of a coefficient of voltage energy efficiency (c_vee), which is proportional to power losses in IMs under PQ disturbances. In this paper, recommendations concerning the implementation of c_vee in the relevant standards are formulated. Additionally, results of PQ monitoring are presented and values of c_vee in land power systems are discussed. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

Current power systems are facing noticeable power quality (PQ) performance deterioration, which has been attributed to nonlinear loads, distributed generation, and extensive renewable energy infiltration (REI). These conditions cause voltage sags, harmonic distortion, flicker, and disadvantageous power factors. The traditional PI/PID-based scheme of control, when applied to Flexible AC Transmission Systems (FACTSs), demonstrates low adaptability and low anticipatory functions, which are required to operate a grid in real-time and dynamic conditions. Artificial Intelligence (AI) opens proactive, reactive, or adaptive and self-optimizing control schemes, which reformulate FACTS to thoughtful, data-intensive power-system objects. This literature review systematically studies the convergence of AI and FACTS technology, with an emphasis on how AI can improve voltage stability, harmonic control, flicker control, and reactive power control in the grid formation of various types of grids. A new classification is proposed for the identification of AI methodologies, including deep learning, reinforcement learning, fuzzy logic, and graph neural networks, according to specific FQ goals and FACTS device categories. This study quantitatively compares AI-enhanced and traditional controllers and uses key performance indicators such as response time, total harmonic distortion (THD), precision of voltage regulation, and reactive power compensation effectiveness. In addition, the analysis discusses the main implementation obstacles, such as data shortages, computational time, readability, and regulatory limitations, and suggests mitigation measures for these issues. The conclusion outlines a clear future research direction towards physics-informed neural networks, federated learning, which facilitates decentralized control, digital twins, which facilitate real-time validation, and multi-agent reinforcement learning, which facilitates coordinated operation. Through the current research synthesis, this study provides researchers, engineers, and system planners with actionable information to create a next-generation AI-FACTS framework that can support resilient and high-quality power delivery. Full article

The power system is evolving with the integration of new technologies, including electronic devices and renewable energy sources, which are increasingly used to support new applications, reduce dependence on fossil fuels, and drive system innovation. However, this shift brings a significant drawback: a reduction in power quality (PQ). The literature extensively discusses the impact of poor PQ on electrical assets and explores potential solutions to this new challenge. Building on this foundation, this paper introduces new PQ indices derived from existing metrics and validated on both synthetic and real signals to assess their effectiveness. The aim is to provide researchers and system operators with simple and efficient tools for the clear identification of PQ issues in monitored networks. These new indices are designed to be flexible and independent of acquisition conditions, making them suitable for a wide range of frequencies (e.g., 50 Hz–150 kHz) and applications. After an overview of the PQ landscape, the paper demonstrates the use of these indices on various voltage waveforms, including a case study from a measurement campaign. The promising results indicate that, when combined with existing indices, these new metrics can form a strong foundation for a deeper understanding and more accurate classification of PQ issues in power networks. Full article

This article designs and validates a P-Q-based shunt active power filter (SAPF) to mitigate voltage harmonics in EERSA’s 13.2 kV feeder 1500080T03. A CYMDIST feeder model, calibrated with field measurements, reveals worst-case voltage THD up to 9.48% due to legacy high-pressure sodium (HPS) street lighting. Co-simulation with a MATLAB/Simulink R2024b, controller guides the sizing of a 150 kVA SAPF at Substation 8. Simulations reduce peak THD at a representative node from 9.48% to 1.51%; replacing HPS with LEDs further improves efficiency while lowering distortion. The retrofit complies with IEEE Std 519-2022, enhances supply reliability, and yields an internal rate of return above 17%, indicating a technically and financially attractive solution for Latin American distribution networks. Full article

The study investigates the electrical energy parameters in the distribution system of a mining facility located in Murmansk Oblast, Russia, focusing on power quality (PQ) issues arising substantially from mine hoist operation conditions. Despite compliance with Russian standards related to PQ, discrepancies were observed between PQ measurement results and problems inherent in the system, such as transformer failures. The research employed two instruments, Resurs-UF2M and Metrel MI2892, to conduct a PQ survey, comparing their data aggregation methods and measurement accuracy. Various data aggregation intervals were also used to evaluate the impact of resolution on PQ assessment. Results revealed significant discrepancies between the instruments, with Metrel MI2892 providing a more reliable and detailed dataset, while Resurs-UF2M failed to capture rapid transients and enable profound PQ analysis to be performed. The research identified eight PQ indices exceeding permissible levels, attributed to the electromagnetic influence of high-power mining equipment. The findings underscore the limitations of current regulatory frameworks and measurement methods, emphasizing the need for revised standards to improve diagnostic accuracy. The research highlights the importance of proper instrument selection and configuration to mitigate PQ disturbances, prevent equipment failures, and enhance power system reliability in mining facilities. Full article

In the current energy landscape, power quality (PQ) emerges as a critical concern. Even when there is no fault on a line, PQ issues are common in all power networks since 90% of power systems’ loads are variable or inductive in nature. Variable loads cannot be avoided; hence, PQ concerns such as voltage swelling and sag will always arise. Voltage sag is one of the main issues within a distribution network, resulting in financial losses for the utility company and the customer. The Dynamic Voltage Restorer (DVR) effectively addresses voltage sags and minimizes total harmonic distortion (THD) in the distribution network. This paper proposed a novel control strategy to increase the PQ in a system. A Frilled Lizard Optimization-optimized fuzzy PI controller is proposed in this work to control the inverter. This proposed method improves the DVR’s ability to correct voltage sag and reduce total harmonic distortion as soon as possible. The PI control scheme is utilized initially to reduce the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the PI method is applied to a fuzzy logic-based compensatory stage. The proposed approach is validated using pro-type models, as well as mathematical and Simulink modelling. In the Results Section, the performance of the proposed controllers with the DVR is tabulated and compared with other DVR controller schemes described in other research papers. Full article

Modern utility systems are being heavily strained by rising energy consumption and dynamic load variations, which have an impact on the quality and reliability of the supply. Harmonic injection and reactive power imbalance are caused by the widespread divergence. Power quality (PQ) issues are mostly caused by renewable energy powered by power electronic converters that are integrated into the utility grid, despite the fact that a range of industries require high-quality power to function properly at all times. Several solutions have been created, but continuing efforts and newly improved solutions are needed to solve these problems by operating according to various international standards. Distributed Static Compensator (DSTATCOM) was created in the proposed model to enhance PQ in a standard bus system. A standard bus system using the DSTATCOM model was initially developed. A real-time dataset was gathered while applying various PQ disturbance conditions. A deep learning controller was created using this generated dataset, which examined the bus voltages to generate the DSTATCOM pulse signal. Two case studies, the IEEE 13 bus and the IEEE 33 bus system, were used to analyze the proposed work. Performance of the proposed deep learning controller was verified in various situations, including interruption, swell, harmonics, and sag. The outcome of THD in the IEEE 13 bus is 0.09% at the sag period, 0.08% at the swell period, 0.01% at the interruption period, and in the IEEE 33 bus was 1.99% at the sag period, 0.44% at the swell period, and 0.01% at the interruption period. Also, the effectiveness of the proposed deep learning controller was examined and contrasted with current methods like K-Nearest Neighbor (KNN) and Feed Forward Neural Network (FFNN). The validated results show that the suggested method provides an efficient mitigation mechanism, making it suitable for all cases involving PQ issues. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

The paper presents an FPGA-based, hardware-accelerated IEC 61850-9-2 Sampled Measured Values (SMV) subscriber—termed the high-speed SMV subscriber (HS3)—by integrating real-time energy-quality (EQ) analytics directly into the subscriber pipeline while preserving a deterministic, microsecond-scale operation under high stream counts. Building on a prior hardware decoder that achieved sub-3 μs SMV parsing for up to 512 subscribed svIDs with modest logic utilization (<8%), the proposed design augments the pipeline with fixed-point RTL modules for single-bin DFT frequency estimation, windowed true-RMS computation, and per-sample active power evaluation, all operating in a streaming fashion with configurable windows and resolutions. A lightweight software layer performs only residual scalar combinations (e.g., apparent power, form factor) on pre-aggregated hardware outputs, thereby minimizing CPU load and memory traffic. The paper’s aim is to bridge the gap between software-centric analytics—common in toolkit-based deployments—and fixed-function commercial firmware, by delivering an open, modular architecture that co-locates SMV subscription and EQ pre-processing in the same hardware fabric. Implementation on an MPSoC platform demonstrates that integrating EQ analytics does not compromise the efficiency or accuracy of the primary decoding path and sustains the latency targets required for protection-and-control use cases, with accuracy consistent with offline references across representative test waveforms. In contrast to existing solutions that either compute PQ metrics post-capture in software or offer limited in-FPGA analytics, the main contributions lie in a cohesive, resource-efficient integration that exposes continuous, per-channel EQ metrics at microsecond granularity, together with an implementation-level characterization (latency, resource usage, and error against reference calculations) evidencing suitability for real-time substation automation. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article