Pub Date : 2025-12-04DOI: 10.1016/j.prime.2025.101140
Hajjar Mamouni , Karim EL Khadiri , Mounir Ouremchi , Mohammed Ouazzani Jamil , Hassan Qjidaa
This paper proposes an on-chip integrated power management system with a Distributed Maximum Power Point Tracking (DMPPT) controller for photovoltaic (PV) cells to enhance energy extraction efficiency in partial shading and inhomogeneous conditions.
Each PV cell is allocated a single MPPT unit to achieve localized power maximization and loss reduction in contrast to centralized tracking systems. The proposed DMPPT controller is realized in 0.18 µm CMOS and integrates Ripple Correlation Control (RCC) and a synchronous boost converter for efficient cell-level tracking. Cadence Virtuoso simulations were carried out using a single-diode PV model at irradiance values from 100 W/m² to 1200 W/m² and a constant temperature of 25°C. The converter runs with a 100 kHz switching frequency, achieving 92 % peak efficiency and stable voltage regulation.
The suggested scheme achieves a mean output voltage of 12.3 V, 986.6 mA of current, and offers nearly twice the normalized power compared to centralized MPPT techniques under partial shading. The chip occupies an area of approximately 1.73 mm². The results verify the engineering feasibility and high efficiency of cell-level Distributed MPPT (DMPPT) for maximizing energy output and operational reliability of photovoltaic systems under non-uniform irradiance.
{"title":"A 0.18 µm CMOS on-chip integrated distributed MPPT (DMPPT) controller for cell-level photovoltaic solar systems","authors":"Hajjar Mamouni , Karim EL Khadiri , Mounir Ouremchi , Mohammed Ouazzani Jamil , Hassan Qjidaa","doi":"10.1016/j.prime.2025.101140","DOIUrl":"10.1016/j.prime.2025.101140","url":null,"abstract":"<div><div>This paper proposes an on-chip integrated power management system with a Distributed Maximum Power Point Tracking (DMPPT) controller for photovoltaic (PV) cells to enhance energy extraction efficiency in partial shading and inhomogeneous conditions.</div><div>Each PV cell is allocated a single MPPT unit to achieve localized power maximization and loss reduction in contrast to centralized tracking systems. The proposed DMPPT controller is realized in 0.18 µm CMOS and integrates Ripple Correlation Control (RCC) and a synchronous boost converter for efficient cell-level tracking. Cadence Virtuoso simulations were carried out using a single-diode PV model at irradiance values from 100 W/m² to 1200 W/m² and a constant temperature of 25°C. The converter runs with a 100 kHz switching frequency, achieving 92 % peak efficiency and stable voltage regulation.</div><div>The suggested scheme achieves a mean output voltage of 12.3 V, 986.6 mA of current, and offers nearly twice the normalized power compared to centralized MPPT techniques under partial shading. The chip occupies an area of approximately 1.73 mm². The results verify the engineering feasibility and high efficiency of cell-level Distributed MPPT (DMPPT) for maximizing energy output and operational reliability of photovoltaic systems under non-uniform irradiance.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"15 ","pages":"Article 101140"},"PeriodicalIF":0.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.prime.2025.101133
Muhammad Syahril Mubarok , Nur Vidia Laksmi B． , Ananta Adhi Wardana , Agus Mukhlisin , Dimas Herjuno
This study introduces a modulated triple vectors model predictive current controller. The proposed method employs a triple voltage vector approach with an optimized duty cycle modulation scheme to improve current prediction accuracy while minimizing torque and current ripples. The control algorithm utilizes cost function minimization for eight possible voltage vectors to determine the optimal current prediction and properly selects voltage vector combination, thereby enhancing dynamic response and steady-state performance. By properly selecting voltage vectors based on the reference position in a stationary reference frame, the proposed method reduces computational complexity compared to conventional single vector and dual vector approaches. In addition, a model predictive speed controller with constraints is implemented to improve the dynamic speed controller. Experimental results confirm the advantages of the proposed method by significantly reducing total harmonic distortion and torque ripple, which are 5,26 % and 0105 N.m, respectively. Additionally, the proposed method exhibits improved robustness under different speed and load disturbance conditions, making this proposed method become a possible solution for high-performance permanent magnet synchronous motor drive applications.
{"title":"A modulated triple vectors model predictive controllers for PMSM drives based on voltage reference position selection","authors":"Muhammad Syahril Mubarok , Nur Vidia Laksmi B． , Ananta Adhi Wardana , Agus Mukhlisin , Dimas Herjuno","doi":"10.1016/j.prime.2025.101133","DOIUrl":"10.1016/j.prime.2025.101133","url":null,"abstract":"<div><div>This study introduces a modulated triple vectors model predictive current controller. The proposed method employs a triple voltage vector approach with an optimized duty cycle modulation scheme to improve current prediction accuracy while minimizing torque and current ripples. The control algorithm utilizes cost function minimization for eight possible voltage vectors to determine the optimal current prediction and properly selects voltage vector combination, thereby enhancing dynamic response and steady-state performance. By properly selecting voltage vectors based on the reference position in a stationary reference frame, the proposed method reduces computational complexity compared to conventional single vector and dual vector approaches. In addition, a model predictive speed controller with constraints is implemented to improve the dynamic speed controller. Experimental results confirm the advantages of the proposed method by significantly reducing total harmonic distortion and torque ripple, which are 5,26 % and 0105 N.m, respectively. Additionally, the proposed method exhibits improved robustness under different speed and load disturbance conditions, making this proposed method become a possible solution for high-performance permanent magnet synchronous motor drive applications.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101133"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rapid growth of plug-in electric vehicles (PEVs) is reshaping demand in low-voltage microgrids where voltage stability and power-quality margins are tight. Uncoordinated charging deepens evening peaks, stresses feeder limits, and constrains renewable hosting. This paper proposes a centralized, optimization-based scheduling strategy for bidirectional charging coordinating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) dispatch to jointly minimize energy cost and enhance voltage stability. A linear programming (LP) model optimizes charging/discharging over discrete intervals subject to realistic constraints: charger power limits, state-of-charge (SoC) bounds, nodal-voltage regulation, and line-flow limits. The optimization is embedded in a forward-backward sweep load-flow loop to respect feeder physics. Using the IEEE European LV 8-bus system, we evaluate five scenarios single tariff, time-of-use (ToU) tariff, holiday load growth, ToU under holiday load, and photovoltaic (PV) integration. Relative to an uncontrolled baseline, the centralized strategy shifts demand off-peak, reduces peaks by up to 40% (12.0 to 7.2 kW), lowers energy cost by up to 25% (₹192.0 to ₹144.0), and improves minimum node voltages to 400–407 V; with PV, energy cost reaches ₹96.0 and minimum voltage rises to 412 V, all within EN 50,160 (±10%) bounds. These results validate a practical, scalable demand-side management (DSM) approach that improves reliability, reduces operating cost, and facilitates renewable integration; extensions to real-time, data-driven, or decentralized variants for larger fleets are outlined.
{"title":"Optimal centralized scheduling strategy for bidirectional charging of PEV fleets in low-voltage microgrids","authors":"Subhasis Panda , Buddhadeva Sahoo , Indu Sekhar Samanta , Pravat Kumar Rout , Binod Kumar Sahu , Mohit Bajaj , Cansu Ayvaz Güven , Vojtech Blazek , Lukas Prokop","doi":"10.1016/j.prime.2025.101136","DOIUrl":"10.1016/j.prime.2025.101136","url":null,"abstract":"<div><div>Rapid growth of plug-in electric vehicles (PEVs) is reshaping demand in low-voltage microgrids where voltage stability and power-quality margins are tight. Uncoordinated charging deepens evening peaks, stresses feeder limits, and constrains renewable hosting. This paper proposes a centralized, optimization-based scheduling strategy for bidirectional charging coordinating grid-to-vehicle (G2V) and vehicle-to-grid (V2G) dispatch to jointly minimize energy cost and enhance voltage stability. A linear programming (LP) model optimizes charging/discharging over discrete intervals subject to realistic constraints: charger power limits, state-of-charge (SoC) bounds, nodal-voltage regulation, and line-flow limits. The optimization is embedded in a forward-backward sweep load-flow loop to respect feeder physics. Using the IEEE European LV 8-bus system, we evaluate five scenarios single tariff, time-of-use (ToU) tariff, holiday load growth, ToU under holiday load, and photovoltaic (PV) integration. Relative to an uncontrolled baseline, the centralized strategy shifts demand off-peak, reduces peaks by up to 40% (12.0 to 7.2 kW), lowers energy cost by up to 25% (₹192.0 to ₹144.0), and improves minimum node voltages to 400–407 V; with PV, energy cost reaches ₹96.0 and minimum voltage rises to 412 V, all within EN 50,160 (±10%) bounds. These results validate a practical, scalable demand-side management (DSM) approach that improves reliability, reduces operating cost, and facilitates renewable integration; extensions to real-time, data-driven, or decentralized variants for larger fleets are outlined.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101136"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-13DOI: 10.1016/j.prime.2025.101135
Ahmed M. Elkholy , Andrew V. Chasov , Dmitry I. Panfilov
Zigzag transformers are essential components in modern power systems for grounding, neutral point establishment, and harmonic filtering in FACTS devices, smart grids, and arc suppression systems. These transformers exhibit highly nonlinear behavior with core saturation and hysteresis effects, present significant challenges for accurate modeling in modern power system analysis. The practical engineering challenge of developing reliable models without access to proprietary internal parameters represents a critical gap in power system analysis, creating significant modeling challenges when internal parameters remain inaccessible due to operational constraints, safety requirements, and intellectual property limitations. This paper introduces a statistical, non-invasive methodology for modeling zigzag transformers using variable impedance derived from external testing procedures including DC resistance measurements, three-phase no-load tests, and single-phase open-circuit tests—all conducted without internal access requirements. Advanced polynomial curve-fitting techniques with rigorous error analysis create a black-box model capturing variable impedance characteristics across the tested frequency range. The model achieves validated accuracy with correlation coefficients for voltage predictions and for current predictions. Validation through MATLAB Simulink simulations and experimental grid verification demonstrates effective transformer performance prediction under various operating conditions, enabling power system analysis without detailed internal information while enhancing stability and reliability for FACTS devices and arc suppression systems.
{"title":"Statistical approach to non-invasive modeling of zigzag transformers using variable impedance","authors":"Ahmed M. Elkholy , Andrew V. Chasov , Dmitry I. Panfilov","doi":"10.1016/j.prime.2025.101135","DOIUrl":"10.1016/j.prime.2025.101135","url":null,"abstract":"<div><div>Zigzag transformers are essential components in modern power systems for grounding, neutral point establishment, and harmonic filtering in FACTS devices, smart grids, and arc suppression systems. These transformers exhibit highly nonlinear behavior with core saturation and hysteresis effects, present significant challenges for accurate modeling in modern power system analysis. The practical engineering challenge of developing reliable models without access to proprietary internal parameters represents a critical gap in power system analysis, creating significant modeling challenges when internal parameters remain inaccessible due to operational constraints, safety requirements, and intellectual property limitations. This paper introduces a statistical, non-invasive methodology for modeling zigzag transformers using variable impedance derived from external testing procedures including DC resistance measurements, three-phase no-load tests, and single-phase open-circuit tests—all conducted without internal access requirements. Advanced polynomial curve-fitting techniques with rigorous error analysis create a black-box model capturing variable impedance characteristics across the tested frequency range. The model achieves validated accuracy with correlation coefficients <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>></mo><mn>0</mn><mo>.</mo><mn>95</mn></mrow></math></span> for voltage predictions and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>></mo><mn>0</mn><mo>.</mo><mn>92</mn></mrow></math></span> for current predictions. Validation through MATLAB Simulink simulations and experimental grid verification demonstrates effective transformer performance prediction under various operating conditions, enabling power system analysis without detailed internal information while enhancing stability and reliability for FACTS devices and arc suppression systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101135"},"PeriodicalIF":0.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.prime.2025.101134
María del Pilar Buitrago-Villada, Carlos E. Murillo-Sánchez
The growing penetration of renewable energy sources and the increasing complexity of modern power systems demand more accurate and computationally efficient operational planning tools, as the associated optimization problems are inherently high-dimensional and computationally intensive. Traditional optimization approaches often rely on simplified DC or convex formulations, which limit their ability to capture the nonlinear behavior of AC network model. This study addresses this gap by proposing a scalable solution framework for the Multi-Period Secure Stochastic AC Optimal Power Flow (MPSSOPF-AC). The proposed approach is based on Generalized Benders Decomposition (GBD) with reformulated AC subproblems that incorporate reserve and storage scheduling. Algorithmic performance is further enhanced through a bundle–trust-region stabilization technique and the parallel solution of subproblems that exploit the problem structure. The proposed methodology is validated on the real-size Colombian 96-bus power system under several wind generation scenarios and N-1 contingencies. Results demonstrate that the proposed GBD-based framework preserves modeling accuracy while reducing computational time by up to 94.8% compared with conventional methods. The outcomes highlight the potential of decomposition-based strategies to enable realistic large-scale stochastic AC-OPF applications in modern power system operation and planning.
{"title":"A stabilized Benders decomposition approach for solving the multi-period secure stochastic AC optimal power flow for energy, reserves, and storage scheduling","authors":"María del Pilar Buitrago-Villada, Carlos E. Murillo-Sánchez","doi":"10.1016/j.prime.2025.101134","DOIUrl":"10.1016/j.prime.2025.101134","url":null,"abstract":"<div><div>The growing penetration of renewable energy sources and the increasing complexity of modern power systems demand more accurate and computationally efficient operational planning tools, as the associated optimization problems are inherently high-dimensional and computationally intensive. Traditional optimization approaches often rely on simplified DC or convex formulations, which limit their ability to capture the nonlinear behavior of AC network model. This study addresses this gap by proposing a scalable solution framework for the Multi-Period Secure Stochastic AC Optimal Power Flow (MPSSOPF-AC). The proposed approach is based on Generalized Benders Decomposition (GBD) with reformulated AC subproblems that incorporate reserve and storage scheduling. Algorithmic performance is further enhanced through a bundle–trust-region stabilization technique and the parallel solution of subproblems that exploit the problem structure. The proposed methodology is validated on the real-size Colombian 96-bus power system under several wind generation scenarios and N-1 contingencies. Results demonstrate that the proposed GBD-based framework preserves modeling accuracy while reducing computational time by up to 94.8% compared with conventional methods. The outcomes highlight the potential of decomposition-based strategies to enable realistic large-scale stochastic AC-OPF applications in modern power system operation and planning.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101134"},"PeriodicalIF":0.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing integration of renewable energy sources (RESs) and battery energy storage systems (BESSs) into hybrid AC/DC microgrids offers opportunities for cost reduction and flexibility but poses challenges in control. This paper proposes a PSO-tuned rule-based energy management system (EMS) that coordinates photovoltaic (PV) generation, BESS, and the utility grid under dynamic pricing. The framework integrates price-based demand response (DR), adaptive battery operation rules, and real-time forecasts to minimize energy consumption cost (ECC). Compared with Genetic Algorithms, PSO achieves faster convergence and higher computational efficiency. A case study at an educational institution demonstrates significant seasonal ECC reductions—39.4 % in autumn, 76.5 % in winter, 65.0 % in summer, and 79.5 % in spring—resulting in annual savings of 64.97 % (from INR 3.40 million to INR 1.19 million). The EMS ensures intelligent load shifting, optimal battery utilization, and zero grid import during peak tariffs while enabling surplus PV injection. Results confirm the proposed approach as a scalable, efficient, and practical solution for reducing costs, improving renewable self-consumption, and enhancing resilience in next-generation hybrid microgrids.
{"title":"Optimized rule-based energy management for AC/DC hybrid microgrids using price-based demand response","authors":"Rampelli Manojkumar , Chamakura Krishna Reddy , T Yuvaraj , Mohit Bajaj , Vojtech Blazek","doi":"10.1016/j.prime.2025.101132","DOIUrl":"10.1016/j.prime.2025.101132","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources (RESs) and battery energy storage systems (BESSs) into hybrid AC/DC microgrids offers opportunities for cost reduction and flexibility but poses challenges in control. This paper proposes a PSO-tuned rule-based energy management system (EMS) that coordinates photovoltaic (PV) generation, BESS, and the utility grid under dynamic pricing. The framework integrates price-based demand response (DR), adaptive battery operation rules, and real-time forecasts to minimize energy consumption cost (ECC). Compared with Genetic Algorithms, PSO achieves faster convergence and higher computational efficiency. A case study at an educational institution demonstrates significant seasonal ECC reductions—39.4 % in autumn, 76.5 % in winter, 65.0 % in summer, and 79.5 % in spring—resulting in annual savings of 64.97 % (from INR 3.40 million to INR 1.19 million). The EMS ensures intelligent load shifting, optimal battery utilization, and zero grid import during peak tariffs while enabling surplus PV injection. Results confirm the proposed approach as a scalable, efficient, and practical solution for reducing costs, improving renewable self-consumption, and enhancing resilience in next-generation hybrid microgrids.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101132"},"PeriodicalIF":0.0,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1016/j.prime.2025.101131
Mina Valikhany , Poria Astero , Matti Lehtonen , Pasi Peltoniemi
Modern power grids are increasingly challenged by the lack of inertia caused by the high penetration of renewable energy sources (RES). This low inertia leads to reduced frequency stability and greater vulnerability to disturbances. To address this issue, various virtual inertia (VI) provision strategies have been proposed to emulate inertial behaviour using power electronic converters and advanced control techniques. However, the existing literature reveals two major research gaps. First, there is no unified understanding of VI classification frameworks, as many studies have used diverse categorizations and often treated the Virtual Synchronous Machine (VSM) and Virtual Synchronous Generator (VSG) as equivalent concepts, leading to conceptual ambiguity between grid-forming (GFM) and grid-following (GFL) approaches. Second, most previous research has examined one or a few VI control technologies in isolation, without providing a comprehensive cross-technology comparison that evaluates their relative suitability and dynamic performance under varying conditions. This review addresses these gaps by proposing a new classification framework, which distinctly differentiates between the VSM, VSG, and Synchronverter concepts, while also emphasizing both inertia provision and inertia emulation aspects. This refined framework enhances the understanding of how various VI-based converters contribute to grid stability through either the active production or the imitation of inertial response. Furthermore, the paper provides a structured and comparative review of VI strategies across multiple renewable energy applications—including electrolyzers, electric vehicles (EVs), battery energy storage systems (BESS), high-voltage direct current (HVDC) systems, wind turbines (WTs), and solar photovoltaic (PV) systems—based on their control architectures, frequency response capabilities, and integration potential in future low-inertia grids. The outcomes of this study aim to support researchers and system operators in selecting and developing appropriate virtual inertia control (VIC) methods for maintaining frequency stability in evolving power systems.
{"title":"A review on the suitability of virtual inertia strategies for the next generation of low-inertia power systems","authors":"Mina Valikhany , Poria Astero , Matti Lehtonen , Pasi Peltoniemi","doi":"10.1016/j.prime.2025.101131","DOIUrl":"10.1016/j.prime.2025.101131","url":null,"abstract":"<div><div>Modern power grids are increasingly challenged by the lack of inertia caused by the high penetration of renewable energy sources (RES). This low inertia leads to reduced frequency stability and greater vulnerability to disturbances. To address this issue, various virtual inertia (VI) provision strategies have been proposed to emulate inertial behaviour using power electronic converters and advanced control techniques. However, the existing literature reveals two major research gaps. First, there is no unified understanding of VI classification frameworks, as many studies have used diverse categorizations and often treated the Virtual Synchronous Machine (VSM) and Virtual Synchronous Generator (VSG) as equivalent concepts, leading to conceptual ambiguity between grid-forming (GFM) and grid-following (GFL) approaches. Second, most previous research has examined one or a few VI control technologies in isolation, without providing a comprehensive cross-technology comparison that evaluates their relative suitability and dynamic performance under varying conditions. This review addresses these gaps by proposing a new classification framework, which distinctly differentiates between the VSM, VSG, and Synchronverter concepts, while also emphasizing both inertia provision and inertia emulation aspects. This refined framework enhances the understanding of how various VI-based converters contribute to grid stability through either the active production or the imitation of inertial response. Furthermore, the paper provides a structured and comparative review of VI strategies across multiple renewable energy applications—including electrolyzers, electric vehicles (EVs), battery energy storage systems (BESS), high-voltage direct current (HVDC) systems, wind turbines (WTs), and solar photovoltaic (PV) systems—based on their control architectures, frequency response capabilities, and integration potential in future low-inertia grids. The outcomes of this study aim to support researchers and system operators in selecting and developing appropriate virtual inertia control (VIC) methods for maintaining frequency stability in evolving power systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101131"},"PeriodicalIF":0.0,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-21DOI: 10.1016/j.prime.2025.101130
Ali Zaki Mohammed Nafa , Adel A. Obed , Ahmed J. Abid , Salam J. Yaqoob , Mohit Bajaj , Vojtech Blazek
Precise estimation of solar irradiance is fundamental for the effective operation, monitoring, and forecasting of photovoltaic (PV) systems. Pyranometers are the standard for solar irradiance measurement, but their cost, sensitivity, and frequent recalibration make them less practical for large-scale use. This study proposes a novel data-driven approach for real-time irradiance prediction based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed model uniquely leverages directly measurable electrical parameters of PV modules—namely, the voltage at the maximum power point (Vmp), current at the maximum power point (Imp), and cell temperature (T)—to predict solar irradiance (G) without requiring module disconnection. This enables continuous, non-invasive monitoring, suitable for embedded and grid-connected PV systems. A synthetic dataset comprising 4000 samples was generated using a MATLAB-based Single-Diode Model (SDM) for a SunPower SPR-X20-250-BLK PV module. Simulations were conducted under four temperature conditions (15°C, 25°C, 45°C, and 65°C) and ten irradiance levels (100 to 1000 W/m²), resulting in 40 (I–V) and (P–V) curves representing diverse environmental scenarios. The ANFIS model was trained and evaluated using eight different membership function (MFs) types and varying MFs counts. The optimal configuration—Gaussian membership function (gaussmf) with 10 MFs—achieved outstanding predictive performance with a Root Mean Square Error (RMSE) of 1.05689 W/m², Mean Absolute Error (MAE) of 0.41864 W/m². Further, an experimental validation was conducted using a custom-built Internet of Things (IoT)-based PV monitoring system comprising three 8 W PV modules (total power: 24 W), an ESP32-based data acquisition unit, and a solar panel WS400A multimeter. The system recorded Vmp, Imp, Voc, Isc, and T under real outdoor conditions. The trained ANFIS model, when tested on this experimental data, yielded a predicted irradiance value of 806.30 W/m² with an RMSE of 0.0328 W/m², affirming the model’s capability to maintain high accuracy under minimal input variation. This research demonstrates the efficacy of ANFIS for solar irradiance prediction using operational PV data, offering a viable alternative to traditional measurement systems.
{"title":"Adaptive neuro-fuzzy modeling for real-time solar irradiance prediction using PV module operating parameters","authors":"Ali Zaki Mohammed Nafa , Adel A. Obed , Ahmed J. Abid , Salam J. Yaqoob , Mohit Bajaj , Vojtech Blazek","doi":"10.1016/j.prime.2025.101130","DOIUrl":"10.1016/j.prime.2025.101130","url":null,"abstract":"<div><div>Precise estimation of solar irradiance is fundamental for the effective operation, monitoring, and forecasting of photovoltaic (PV) systems. Pyranometers are the standard for solar irradiance measurement, but their cost, sensitivity, and frequent recalibration make them less practical for large-scale use. This study proposes a novel data-driven approach for real-time irradiance prediction based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed model uniquely leverages directly measurable electrical parameters of PV modules—namely, the voltage at the maximum power point (V<sub>mp</sub>), current at the maximum power point (I<sub>mp</sub>), and cell temperature (T)—to predict solar irradiance (G) without requiring module disconnection. This enables continuous, non-invasive monitoring, suitable for embedded and grid-connected PV systems. A synthetic dataset comprising 4000 samples was generated using a MATLAB-based Single-Diode Model (SDM) for a SunPower SPR-X20-250-BLK PV module. Simulations were conducted under four temperature conditions (15°C, 25°C, 45°C, and 65°C) and ten irradiance levels (100 to 1000 W/m²), resulting in 40 (I–V) and (P–V) curves representing diverse environmental scenarios. The ANFIS model was trained and evaluated using eight different membership function (MFs) types and varying MFs counts. The optimal configuration—Gaussian membership function (gaussmf) with 10 MFs—achieved outstanding predictive performance with a Root Mean Square Error (RMSE) of 1.05689 W/m², Mean Absolute Error (MAE) of 0.41864 W/m². Further, an experimental validation was conducted using a custom-built Internet of Things (IoT)-based PV monitoring system comprising three 8 W PV modules (total power: 24 W), an ESP32-based data acquisition unit, and a solar panel WS400A multimeter. The system recorded V<sub>mp</sub>, I<sub>mp</sub>, V<sub>oc</sub>, I<sub>sc</sub>, and T under real outdoor conditions. The trained ANFIS model, when tested on this experimental data, yielded a predicted irradiance value of 806.30 W/m² with an RMSE of 0.0328 W/m², affirming the model’s capability to maintain high accuracy under minimal input variation. This research demonstrates the efficacy of ANFIS for solar irradiance prediction using operational PV data, offering a viable alternative to traditional measurement systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101130"},"PeriodicalIF":0.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-16DOI: 10.1016/j.prime.2025.101128
Amir Hossein Babaali, Mohammad Taghi Ameli
Imbalanced databases tend to bias machine learning models toward the majority class, compromising the accuracy of network state assessment and leading to suboptimal or erroneous decision-making. This study addresses the issue of data imbalance by proposing a synthetic data generation approach based on a Generative Adversarial Network (GAN). The proposed model employs a conditional Wasserstein GAN with a gradient penalty. A Gated Recurrent Unit (GRU) network integrated with an attention mechanism is utilized to generate diverse, high-quality, and realistic data. The experiments are conducted on the IEEE 118-bus and a real-world network. The findings show that the proposed method can effectively produce realistic, high-quality samples for minority classes. In addition to accuracy, performance is evaluated using metrics such as Misdetection (Mis), False Alarm (FA), and G-mean. The model’s robustness is validated under topology changes and varying imbalance ratios. Findings from the real-world network demonstrate resilient performance and promising results in STVS assessment.
{"title":"Multi-class imbalanced learning for short-term voltage stability assessment","authors":"Amir Hossein Babaali, Mohammad Taghi Ameli","doi":"10.1016/j.prime.2025.101128","DOIUrl":"10.1016/j.prime.2025.101128","url":null,"abstract":"<div><div>Imbalanced databases tend to bias machine learning models toward the majority class, compromising the accuracy of network state assessment and leading to suboptimal or erroneous decision-making. This study addresses the issue of data imbalance by proposing a synthetic data generation approach based on a Generative Adversarial Network (GAN). The proposed model employs a conditional Wasserstein GAN with a gradient penalty. A Gated Recurrent Unit (GRU) network integrated with an attention mechanism is utilized to generate diverse, high-quality, and realistic data. The experiments are conducted on the IEEE 118-bus and a real-world network. The findings show that the proposed method can effectively produce realistic, high-quality samples for minority classes. In addition to accuracy, performance is evaluated using metrics such as Misdetection (Mis), False Alarm (FA), and G-mean. The model’s robustness is validated under topology changes and varying imbalance ratios. Findings from the real-world network demonstrate resilient performance and promising results in STVS assessment.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101128"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Measuring the series capacitance of a transformer winding is a challenging task because it cannot be directly measured with standard devices. This paper presents an indirect method to determine this key parameter, relying on only two measurements: the equivalent capacitance and the total ground capacitance. These measurements are obtained directly from the frequency response impedance (FRI) data collected with the neutral open. The proposed methodology is based on an analytical relationship derived from a simplified lumped equivalent circuit for the transformer winding. This approach converts the lumped model capacitances into a second-degree polynomial function that varies with the unknown series capacitance. The method enables estimation of the winding's series capacitance, regardless of the number of sections in the equivalent circuit. It is applied practically to two different cases of transformer winding. Results showed that the method is both efficient and simple to implement, even in complex models. Comparisons with other estimation techniques confirmed its accuracy and effectiveness. Determining the series capacitance is essential for creating a comprehensive transformer winding model, as this parameter is vital for fault analysis and diagnosis.
{"title":"Accurate estimation of series capacitance using the high-frequency lumped model of transformer winding from FRI data measurements: An indirect measurement procedure","authors":"Billel Allouane , Samir Moulahoum , Moustafa Sahnoune Chaouche","doi":"10.1016/j.prime.2025.101129","DOIUrl":"10.1016/j.prime.2025.101129","url":null,"abstract":"<div><div>Measuring the series capacitance of a transformer winding is a challenging task because it cannot be directly measured with standard devices. This paper presents an indirect method to determine this key parameter, relying on only two measurements: the equivalent capacitance and the total ground capacitance. These measurements are obtained directly from the frequency response impedance (FRI) data collected with the neutral open. The proposed methodology is based on an analytical relationship derived from a simplified lumped equivalent circuit for the transformer winding. This approach converts the lumped model capacitances into a second-degree polynomial function that varies with the unknown series capacitance. The method enables estimation of the winding's series capacitance, regardless of the number of sections in the equivalent circuit. It is applied practically to two different cases of transformer winding. Results showed that the method is both efficient and simple to implement, even in complex models. Comparisons with other estimation techniques confirmed its accuracy and effectiveness. Determining the series capacitance is essential for creating a comprehensive transformer winding model, as this parameter is vital for fault analysis and diagnosis.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"14 ","pages":"Article 101129"},"PeriodicalIF":0.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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