Electricity, Volume 7, Issue 1 (March 2026) – 24 articles

Secondary power supplies are an integral part of any complex device that requires power to different circuit nodes. This includes various kinds of telecommunication equipment, the aerospace industry, battery chargers, etc. Secondary power supplies include the most common pulse converters of both the boost, buck, and buck–boost variety, as well as forward, flyback, and push–pull converters. In particular, a galvanic isolation option may be considered for push–pull types. The use of multi–channel secondary power supplies is relevant for the space industry and satellites, where it is necessary to support the operation of many related devices. The efficiency of such devices is high due to their small number of elements and their simplicity of control. PWM (pulse width modulation) controllers can be considered as the last statement. In turn, the presence of radiation-resistant CMOS technology is required in outer space conditions, which is possessed by the PWM controller considered in this paper. Also, high efficiency and small dimensions can be achieved using planar technology. Here, one such secondary power supply, based on the PWM controller 5315EU015 with a power of 10 W, is considered, as well as the proposed design of a planar transformer. A mathematical model obtained from the algebraization of differential equations method, and from the PSIM software v. 22.2 simulation results and experiments is presented. Full article

Non-intrusive load monitoring (NILM) can provide actionable feedback for demand response (DR) when direct measurements of device states are unavailable. We propose a reproducible, engineering-oriented pipeline for detecting ON/OFF states of end-use load groups from an aggregated active power time series. The method uses robust hysteresis-based labeling with adaptive thresholds derived from the median and median absolute deviation, followed by compact feature engineering restricted to global active power (GAP). After removing collinear features (|r| > 0.98), permutation importance is used to retain informative predictors. Probabilistic binary classifiers (LGBM, Histogram-based Gradient Boosting, XGBoost, and CatBoost) are trained for each target load, and the decision threshold is optimized via F_β to balance missed events and false alarms. A post-processing stage stabilizes predictions by smoothing probabilities and suppressing spurious triggers. Model quality is assessed with both sample-wise metrics and event-based metrics that credit the correct detection of switching intervals within a time tolerance. Experiments on the open “Individual Household Electric Power Consumption” dataset (1-min resolution, 2007–2010) demonstrate that lightweight gradient boosting models, particularly LGBM, deliver reliable and interpretable state estimates suitable for practical DR integration and edge deployment. Full article

This study presents the use of a Battery Energy Storage System (BESS) and a thermal power plant to enhance Primary Frequency Regulation (PFR) in a power system. This integration seeks to mitigate operational challenges, such as the reduction in system inertia and frequency regulation, which are heightened when increasing renewable energy use in power grids with high hydroelectric generation. The proposed solution enables thermal generators to operate at optimal capacity, while the BESS provides a rapid frequency response, thereby enhancing operational efficiency and compliance with national standards. The process was structured in five stages: criteria definition, analysis, design, models, and evaluation. A comprehensive methodological approach was adopted, including dynamic system modeling and BESS sizing based on regulatory parameters. The method was tested with real data from a thermal plant under the conditions of the Colombian electricity market. The simulation results highlight the effectiveness of the proposed BESS, with a response time of approximately 0.6 s and regulation maintenance for over 30 s, reducing mechanical stress and preventing frequency overshoot. The control strategy was designed to maintain the energy neutrality of the BESS, thereby stabilizing its state of charge over the operational horizon. The results show that the BESS targets high-frequency transients and the generator focuses on low-frequency adjustments, managed by an Energy Management System (EMS) with a unified control approach. Full article

Increasing variability in electricity load patterns, driven by end-use behaviour, grid-related technological changes, and socio-economic factors, calls for more accurate and efficient short-term load prediction (STLP) models. This study evaluates the predictive performance of four hybrid models for short-term Amp-load prediction: Adaptive Neuro-Fuzzy Inference System (ANFIS) combined with Genetic Algorithms (GA) and Particle Swarm Optimisation (PSO), as well as convolutional neural networks (CNN) integrated with long short-term memory (LSTM) and extreme gradient boosting (XGB). The models were developed using hourly Amp-load data collected from a power utility substation in Kenya, together with corresponding meteorological data (temperature, wind speed, and humidity) covering a period from January 2023 to June 2024. Results show that the ANFIS-PSO and ANFIS-GA models outperform the CNN-based models, achieving MAPE values of 4.519 and 4.363, RMSE values of 0.3901 and 0.4024, and R² scores of 0.8513 and 0.8481, respectively, due to the adaptive nature of ANFIS, which enables effective modelling of the irregular, nonlinear, and complex temporal behaviour of the Amp load. Enhanced prediction accuracy was observed across all models when variational mode decomposition (VMD) was applied to pre-process the input data. This result was corroborated through further analysis of the Amp-load signals using Taylor plots. Among all of the configurations tested, the CNN-LSTM-VMD model exhibited the highest overall prediction accuracy, with MAPE of 2.625, RMSE of 0.1898, and R² of 0.9702, marginally outperforming the ANFIS-PSO-VMD model, thus making it more suitable for short-term load prediction applications. Full article

The high penetration of wind power, photovoltaic, and energy storage not only presents opportunities for green and low-carbon development, but also poses significant challenges to frequency regulation. During primary frequency regulation (PFR), improper deadband settings may cause delayed or inadequate frequency responses, thereby exacerbating system frequency fluctuations, reducing renewable energy utilization rates, and compromising grid security and stability. This study proposes a parameter optimization method based on deadband to enhance PFR accuracy and improve overall energy conservation and emission reduction benefits. First, the impact of different deadband settings on system frequency fluctuations is analyzed, and the frequency response process is decomposed to quantify its effects on frequency stability and renewable energy integration capacity. Subsequently, the PFR coefficient is modified and optimized with maximum frequency deviation as the objective, thereby strengthening the frequency response capability of renewable energy. Simulation results demonstrate that the modified PFR coefficients reduce the maximum frequency deviation of wind–solar–storage systems by 0.0026 Hz, 0.0036 Hz, and 0.0034 Hz, respectively. This effectively elevates renewable energy integration levels and enhances the low-carbon stability of power system operations. Full article

The use of photovoltaic (PV) water pumping technology offers a viable and sustainable alternative to conventional diesel-driven pumping systems. In PV-based pumping installations, the elimination of bulky transformers significantly reduces the overall system size and weight, which is particularly advantageous for rural and remote irrigation applications. However, removing the transformer can result in high common-mode voltage (CMV) when the induction motor is controlled using a direct torque control (DTC) scheme. This elevated CMV induces leakage currents that may damage the motor, compromise system reliability, and pose potential safety hazards. To ensure a more compact and safer PV pumping system, this paper introduces an improved DTC-based control strategy for induction motors driven by transformerless multilevel PV inverters. The proposed approach effectively suppresses leakage current by mitigating its main source, CMV, while maintaining the simple structure and dynamic performance inherent to conventional DTC. Two new look-up tables (LUTs) are developed to control the stator flux and electromagnetic torque while simultaneously eliminating leakage current. The first method, termed zero-medium vector DTC (ZMV-DTC), employs both zero and medium voltage vectors from the space vector diagram. The second, referred to as medium vector DTC (MV-DTC), utilizes only medium vectors. Numerical simulation results validate the feasibility and superior performance of the proposed algorithms in terms of leakage current suppression. Compared with a conventional DTC (C-DTC) scheme that is designed to limit the CMV, the proposed DTC algorithms achieve a much stronger reduction in the CMV, confining its amplitude to only a few volts, instead of the levels ±V_dc/6 typically produced by the C-DTC. As a result, the leakage current is effectively eliminated, ensuring safer and more reliable operation of the system. Full article

Traditional thermal power systems are merging with distributed generation and renewable energy sources, resulting in complex interconnected power system networks. This results in operational burdens and complexities in thermal power plants that they were not designed to handle. The role of Automatic Voltage Regulation (AVR) is crucial in maintaining the stability and dependability of these complicated power systems. This research provides a comprehensive review of the AVR control strategies within the last five years, considering operational complexities, changing topologies, and evolving challenges, in contemporary power systems. This review first explores the contemporary control strategies used in voltage regulation. Second, it provides an in-depth evaluation of the traditional Proportional Integral Derivative controllers with various improvements, adaptions, and modifications, followed by an examination of supplementary controllers in the AVR framework. Lastly, this paper reviews various optimisation strategies published in the last five years. This paper enriches our understanding of traditional and advanced control strategies in AVR, providing a comprehensive evaluation of their effectiveness and constraints, and aims to provide a valuable resource for researchers in this field. Full article

The rapid integration of renewable energy sources has accelerated the adoption of DC microgrids as an effective platform for flexible and reliable power generation and management. However, conventional droop-based control suffers from inherent limitations, particularly voltage deviations at the DC bus, which compromise stability, power-sharing accuracy, and overall system performance. To address these challenges, this paper presents a distributed secondary control framework for a standalone PV battery-based DC microgrid that achieves bus voltage regulation, precise power distribution, and state-of-charge (SoC) balancing across multiple energy storage units (ESUs). At the primary level, an adaptive mechanism is introduced that dynamically adjusts droop coefficients in response to the real-time SoC of each ESU, promoting balanced utilization of storage resources. At the secondary level, the strategy leverages limited peer-to-peer communication to exchange only aggregate power information, thereby enabling accurate load sharing while preserving scalability and plug-and-play capability. The control architecture further incorporates voltage and current error compensation, with parameters tuned using a Whale Optimization Algorithm to enhance dynamic response. Validation is carried out through a real-time simulation environment developed in MATLAB/Simulink R2024b and executed on a Speedgoat^TM platform. The results demonstrate robust SoC equalization, improved bus voltage stability, and reliable cooperative coordination, positioning the scheme as a practical solution for next-generation DC microgrids. Full article

This study presents an advanced demand-side management framework to optimize energy consumption in smart grids featuring significant intermittent renewable energy integration. The approach leverages real-time data from an advanced metering infrastructure and a predictive model employing a bidirectional long short-term memory network enhanced with attention mechanisms for accurate load and electricity price forecasting. These predictions drive a multi-objective optimization model that harmonizes flexible demands across residential, commercial, and industrial sectors. A novel reference-guided multi-objective particle swarm optimizer is proposed to address the problem’s complexity, promoting improved convergence and diversity in solutions. In benchmarks, RGMOPSO demonstrated superior performance, attaining a fifty-six percent win rate in convergence metrics and a hypervolume of zero point nine three. Simulation results validate the framework’s effectiveness. It achieved a twenty percent reduction in operational costs, a nineteen-point-seven percent lower peak-to-average ratio, and an eighteen percentage point increase in renewable utilization. User-centric benefits included a thirty percent enhancement in comfort and a corresponding reduction in battery degradation. This integrated solution offers a resilient pathway for sustainable smart grid operations amid renewable uncertainties. Full article

Currently, the smart grid concept represents the modern vision of an automated and highly adaptable electrical grid. Supervisory control and data acquisition (SCADA) systems are a fundamental component of a smart grid, enabling communication between field equipment and digital environments. For this purpose, they require industrial frameworks, among which IEC 61850 stands out. IEC 61850 has become a widely adopted standard for substation automation systems (SASs). However, despite its widespread adoption, IEC 61850 faces significant implementation challenges, including the potential complexity of data modeling, which often leads to discrepancies in semantic interpretation and, consequently, different readings among SAS configuration users. A disparity in the semantic interpretation of a process can negatively affect SAS operation, leading to execution errors or interoperability issues. Translating and analyzing SAS configurations can identify and resolve semantic interpretation discrepancies across these systems. The purpose of this research was to determine the degree to which a user interface was perceived as useful to support the translation and analysis of SAS configurations based on the IEC 61850 standard. To this end, a software tool was proposed as the central artifact to address the socio-technical dimension of a custom-built SCADA system at a Latin American state enterprise. The tool serves as the local, intelligent, and real-time operational layer in that system and was rated by users experienced with IEC 61850 as highly usable. The consistently obtained results suggest potential support for those performing the SAS configuration. Full article

This paper presents a novel edge-computing-based architecture for optimal inverse time overcurrent relays installed to protect mesh microgrids (MGs) with distributed generation. The procedure employs graph theory to automate the detection of network changes, fault locations, and relay pairs in an MG. In addition, an automated process obtains the initial protection settings based on the operating conditions of the MG. Furthermore, the Continuous Genetic Algorithm (CGA), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) were implemented to determine the optimal protection settings to obtain better coordination between primary and backup protection relays. These processes were implemented using PowerFactory 2024 Service Pack 5A and Python 3.13.1. The proposal was validated in 68 operating scenarios that considered the islanded and connected operation modes of the MG, charging and discharging cycles of electric vehicle stations, and the presence or absence of photovoltaic generation. The overcurrent protection relays were organized into 100 primary–backup relay pairs to ensure proper coordination and selectivity. The total miscoordination time (TMT) index was used to measure when all pairs of relays were coordinated, with a minimum time close to zero. The results of the graph theory show that all the meshes, fault locations, and relay pairs were identified in the MG. The approach successfully coordinated 100 relay pairs across 68 scenarios, demonstrating its scalability in complex real-world MGs. The automation process obtained an average TMT of 12.2%, while the optimization obtained a TMS of 91.6% with the CGA, and a TMT of 99% was obtained with the SSA and PSO, demonstrating the effectiveness of the optimization process in ensuring selectivity and appropriate fault clearing times. Full article

LED lamps have not been demonstrating the durability claimed by their manufacturers. One hypothesis is that switching transients may contribute to this. This study investigated switching-induced transients in LED lamps operated through dry contacts: manual switches and contactors. Using an oscilloscope, automated acquisition of waveform records was performed while several lamps were switched on in a 220 V_RMS/60 Hz electrical network. LED lamps of different models and manufacturers, one incandescent lamp, and a group of 48 LED lamps, subdivided into six sets of eight lamps, were all switched simultaneously. A total of 56 waveform-record files were obtained from the oscilloscope, comprising 2920 captured screens and 170 measurements. Transient voltage peaks of 380 and 391 V at the supply side, and 357 and 370 V at the lamp side, as well as voltage slew rates of up to 12 and 13 V/µs at the supply side and up to 16 and 19.5 V/µs at the lamp side, were measured, without considering statistical variations, which may indicate values exceeding the ordinary sinusoidal voltage peak (≅311 V) and its typical worst-case slew rate (≅0.12 V/µs). Future studies are suggested, such as tests in real installations, investigations of transient amplification or attenuation within electrical networks, assessment of the effects of wiring and impedance discontinuities, switch bounce, and semiconductor degradation, among others, to continue these studies. Full article

Although interest in tidal energy has increased in recent years, its development remains significantly behind that of other renewable sources such as solar and wind energy. This delay is primarily caused by the complex and harsh ocean environment, which imposes significant constraints on operational systems. This paper proposes a new approach to the design and control of a marine current turbine (MCT) emulator without a pitch mechanism, operating in real time below the rated marine current speed.The emulator control strategy integrates two approaches: predictive control for regulating the speed of the DC machine, and a Linear Quadratic Regulator (LQR) control scheme for maximizing power extraction from the marine current. Our experimental results demonstrate the effectiveness of the proposed hybrid control strategy, which allows precise tracking of reference signals and stable regulation of the direct current machine (DCM) speed, thereby ensuring synchronization with the turbine’s rotational speed. This approach ensures optimal and robust performance over the entire range of marine current variations. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

The increasing integration of hybrid AC/DC networks in modern power systems introduces new challenges in fault detection and grounding scheme design, necessitating advanced techniques for stable and reliable operation. This paper investigates fault detection and grounding schemes in hybrid AC/DC networks using a machine learning (ML) approach to enhance accuracy, speed, and adaptability. Traditional methods often struggle with the dynamic and complex nature of hybrid systems, leading to delayed or incorrect fault identification. To address this, we propose a data-driven ML framework that leverages features such as voltage, current, and frequency characteristics for real-time detection and classification of faults. Additionally, the effectiveness of various grounding schemes is analyzed under different fault conditions to ensure system stability and safety. Simulation results on a hybrid AC/DC test network demonstrate the superior performance of the proposed ML-based fault detection method compared to conventional techniques, achieving high precision, recall, and robustness against noise and varying operating conditions. The findings highlight the potential of ML in improving fault management and grounding strategy optimization for future hybrid power grids. Full article

A wide range of factors affect the dynamic and complex environment that is the commodity market. The most significant of these are external drivers, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are exhibited by individual commodities, which manifest through seasonal patterns and characteristic fluctuations. This study aimed to analyze the day-ahead electricity market and identify the key factors shaping electricity price formation. Particular focus was given to the role of meteorological variables and the interrelationships between the prices of other commodities, such as natural gas, coal, and oil. The analysis combined empirical techniques, such as Fourier transform and correlation analysis, with a predictive LSTM model using a Seq2Seq architecture to forecast short-term electricity prices. A basic forecast of electricity prices in the day-ahead market was provided by a simple predictive model that was developed based on the findings. The results highlight the interconnectedness of energy markets and confirm that external factors play a crucial role in shaping electricity prices. Full article

This paper presents a detection algorithm for identifying when a sinusoidal signal becomes zero, which can provide information about its amplitude. This method can be used to detect voltage interruptions in a single-phase sinusoidal waveform, which may be applied in the rapid recognition of power outages in single-phase electrical systems. The method requires the measurement of a voltage signal. Other analysis methods, like calculating the Root Mean Square (RMS), are based on window sampling and require storing a relatively larger amount of samples in the system memory; an advantage of the proposed method is that it does not require as many samples, but its main advantage is its ability to reduce the detection time compared to other approaches. Techniques like the RMS value or amplitude detection through FFT typically require one full AC cycle to change from a 100% to 0% output signal and then detect a blackout, whereas the proposed method achieves detection within only a quarter cycle without considering additional rate-of-change enhancements, which can be further applied. The algorithm treats the measured single-phase voltage as the α component of an αβ Clarke pair and generates the β component by introducing a 90° electrical delay through a delayed replica of the original signal. The resulting αβ signals are then transformed into the dq reference frame in which the d component is used for outage detection, as it rapidly decreases from 100% to 0% within a quarter cycle following an interruption. This rapid response makes the proposed method suitable for applications that demand minimal detection latency, such as battery backup systems. Both simulation and experimental results validate the effectiveness of the approach. Full article

This paper presents the simultaneous maximization of speed and sensitivity in the Optimal Coordination of Directional Over-Current Protections (OC-DOCP), considering that maximum selectivity is maintained in all solutions. Only these three desirable features of the protection system were considered in the multi-objective approach; thus, the problem can be simply formulated as the weighted sum of speed and sensitivity as goals to be maximized, and the Pareto frontiers correlating speed and sensitivity are easily found in this way. These Pareto frontiers had not been shown in the literature about this topic, and they properly show the compromise solutions for the optimal solutions (i.e., speed improvements imply sensitivity deterioration while sensitivity improvements imply speed degradation). The simplest OC-DOCP formulation, applied to a well-known sample system, is taken as an example to show the Pareto frontiers for different time–current curve types. Another OC-DOCP formulation, which considers different topologies and their probability of occurrence, is also solved and the corresponding Pareto frontiers are also shown. The main findings of this work are the following: (a) in general, the results show that the variation in the speed in the Pareto frontier is more notorious for the less inverse curve types, whose optimal solutions are slower; (b) in the case of extremely inverse curves, the optimal solutions are faster and the effect of changes in sensitivity on the protection speed is very low in the Pareto frontiers; (c) it is also herein shown that the knowledge of this topic is also useful to solve some possible cases of unfeasibility related to the upper bound of time dial settings. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article

In this work, a comprehensive literature review on power-routing devices is presented, outlining their current design principles and potential uses. Additionally, a comprehensive loss model for Modular Multilevel Converters (MMCs) in the context of power routers (PRs), a promising technology for enhancing flexibility and efficiency in future smart and hybrid AC–DC grids. Despite their potential, large-scale PR deployment is still limited by the lack of accurate and validated loss models. To address this gap, a detailed analytical model based on the Marquardt approach is proposed, capturing both conduction and switching losses in converter-based PRs. The model is validated through analytical comparison and PLECS simulations, showing strong agreement with theoretical and experimental data. Four case studies are presented to assess the effect of parameters such as power factor, active and reactive power, and the number of submodules on the overall converter losses. The results demonstrate that PR efficiency improves with optimized converter design and proper parameter selection. Full article

Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that combines Temporal Convolutional Networks (TCNs), Gated Recurrent Units (GRUs), and Random Forests (RFs) in an optimized weighted ensemble strategy. This approach leverages the complementary strengths of each component: TCNs capture long-range temporal dependencies via dilated causal convolutions; GRUs model sequential weather-driven dynamics; and RFs enhance robustness to outliers and nonlinear relationships. The model was evaluated on high-resolution operational data from the Yulara solar plant in Australia, forecasting horizons from 5 min to 1 h. Results show that the TCN-GRU-RF model consistently outperforms conventional benchmarks, achieving R² = 0.9807 (MAE = 0.0136; RMSE = 0.0300) at 5 min and R² = 0.9047 (RMSE = 0.0652) at 1 h horizons. Notably, the degradation in R² across forecasting horizons was limited to 7.7%, significantly lower than the typical 10–15% range observed in the literature, highlighting the model’s scalability and resilience. These validated results indicate that the proposed approach provides a robust, scalable forecasting solution that enhances grid reliability and supports the integration of distributed renewable energy sources. Full article

The progressive replacement of synchronous machines by inverter-based resources (IBRs) reduces system inertia and short-circuit strength, making power systems more vulnerable to frequency and voltage instabilities. Grid-forming (GFM) inverters can mitigate these issues by establishing voltage and frequency references, emulating inertia and enabling autonomous operation during islanding, while grid-following (GFL) inverters mainly contribute to reactive power support. This paper evaluates the capability of GFM inverters to provide grid support under both grid-connected and islanded conditions at the distribution level. Electromagnetic transient (EMT) simulations in MATLAB/Simulink R2022b were performed on a 20 kV radial microgrid comprising GFM and GFL inverters and aggregated load. Small disturbances, including phase-angle jumps and voltage steps at the point of common coupling, were introduced while varying the GFM share and virtual inertia constants. Also, local variables were assessed during islanded operation and separation process. Results indicate that maintaining a GFM share above approximately 30–40% with inertia constants exceeding 2 s significantly enhances frequency stability, supports successful transitions to islanded operation, and improves overall resilience. The study highlights the complementary roles of GFM and GFL in enabling the stable and resilient operation of converter-dominated distribution systems. Full article

Electrical energy in urban and industrial power supply networks is mainly transmitted through 6–10-kV cable networks with an isolated neutral, where most lines are made as bunches of cables. Up to 75–90% of electrical faults in these cable networks belong to single-phase ground faults (SGFs), which can cause more severe accidents accompanied by significant economic damage. Widely known simple and directional protections against SGFs are relatively selective and, hence, often incapable of properly responding to SGFs in a network with such lines and detecting a cable with SGFs in the bunch of a damaged line. These disadvantages can be eliminated by using new, simple, and inexpensive, absolutely selective protections capable of detecting a cable with SGFs in a damaged line. We suggest the techniques and devices based on zero-sequence current transformers and ring measuring converters for building up such protection systems. The methods for calculating zero-sequence currents in cables of a bunched cable line, depending on the SGF point and the currents in the responding elements, are developed, as well as a procedure for determining a damaged cable and methods for estimating the responding element thresholds and the length of the protection dead zone. Full article

Induction motors (IMs) are widely used in variable-speed electric drive systems, where the motor is supplied by a voltage source inverter (VSI). Thus, PWM inverter–IM combination presents several issues that can degrade system performance, particularly overvoltage phenomena when long cables are used. In inverter-fed drive systems, the physical separation between the converter and the motor often requires long motor cables, which can significantly affect voltage stress. As the inverter’s output pulses propagate through the cable, voltage reflections and high-frequency oscillations occur at the motor terminals. We theoretically and experimentally investigate the effect of three PWM methods, namely Space Vector (SVPWM), Selective Harmonic Elimination PWM (SHEPWM), and Random PWM (RPWM) strategies, on overvoltage at the terminals of an induction motor fed by a PWM inverter through a long cable. The simulation results exhibit the validity and efficiency of SVPWM control to reduce overvoltage for different cable lengths. In addition, in order to reduce and eliminate all overvoltage peaks, three filters are proposed and evaluated: an RC filter, an RLC filter, and a compensator. The proposed PWM strategies are assessed using equivalent experimental results obtained on an induction motor fed by a two-level VSI. The experimental tests demonstrate also the efficiency of the SVPWM compared to other strategies. Full article