Pub Date : 2025-07-17DOI: 10.1016/j.prime.2025.101076
Didik Fauzi Dakhlan , Joko Muslim , Indra Kurniawan , Bambang Anggoro Soedjarno , Kevin M. Banjar-Nahor , Nanang Hariyanto
A blackout in the electrical power interconnection system occurs when the stability limits of power system are exceeded. The majority of global power blackouts, including those in Indonesia, are primarily triggered by disturbances within specific segments of the transmission system. These disturbances propagate through interconnected networks, leading to widespread disruptions in the electrical power grid. Oscillations in the electric power system i.e., low-frequency power system oscillations which refer to inter-area oscillations, are distinct phenomena which appear in the interconnection prior to blackout events. These oscillations data are typically obtained from disturbance records after the incidents and used as basis data for post-mortem analysis to describe and identify the oscillating generators in the power system.
The implementation of Phasor Measurement Units (PMUs) for acquiring synchronized phasor data and assessing the potential evolution of system conditions based on these oscillations, prior to significant disruptions in the electrical power system, has been incorporated into IEC/IEEE standards. Additionally, new equipment for synchronized phasor measurement (synchrophasors) utilizing PMUs has been developed in accordance with these standards. A method that is widely used to determine the presence of oscillations in an electric power system is Prony analysis. Prony analysis is a powerful signal processing technique to estimate the parameters of a signal. In power systems, this is applied to estimate the parameters of power system signals, such as voltage, current, and frequency, which are essential for power system control and protection.
This research demonstrates Prony method to monitor oscillations in power system using synchrophasor data, establishing the correlation between oscillation at various busses within the grid and the implication of practical DSF value selection to the model accuracy for selected grid system. A modified Kundur’s four-machine two-area test system model is employed as base model, scaled to the actual measurement data of 45 Extra High Voltage Substation 500 kV in Indonesia power system interconnection. This paper discusses the principles of Prony analysis, its advantages and limitations, including the applications to detect power system oscillation. The performance of Prony analysis is evaluated by comparing the simulation data and real time measurement data from actual Java interconnection system. In practical applications within the selected grid, the DSF to mismatch the actual data and signal construction were then selected and matched as the reference for a particular grid application. The results demonstrate that Prony analysis, when applied to PMU data with a DSF value of 6, provides a reliable and effective method for detecting power system oscillations where the squared errors are reduced from 600–8000% with DSF=2–4 down to 0.03–10% for dominant oscillation
{"title":"Performing Prony method and down sampling factor optimization for power system oscillation analysis","authors":"Didik Fauzi Dakhlan , Joko Muslim , Indra Kurniawan , Bambang Anggoro Soedjarno , Kevin M. Banjar-Nahor , Nanang Hariyanto","doi":"10.1016/j.prime.2025.101076","DOIUrl":"10.1016/j.prime.2025.101076","url":null,"abstract":"<div><div>A blackout in the electrical power interconnection system occurs when the stability limits of power system are exceeded. The majority of global power blackouts, including those in Indonesia, are primarily triggered by disturbances within specific segments of the transmission system. These disturbances propagate through interconnected networks, leading to widespread disruptions in the electrical power grid. Oscillations in the electric power system i.e., low-frequency power system oscillations which refer to inter-area oscillations, are distinct phenomena which appear in the interconnection prior to blackout events. These oscillations data are typically obtained from disturbance records after the incidents and used as basis data for post-mortem analysis to describe and identify the oscillating generators in the power system.</div><div>The implementation of Phasor Measurement Units (PMUs) for acquiring synchronized phasor data and assessing the potential evolution of system conditions based on these oscillations, prior to significant disruptions in the electrical power system, has been incorporated into IEC/IEEE standards. Additionally, new equipment for synchronized phasor measurement (<em>synchrophasors</em>) utilizing PMUs has been developed in accordance with these standards. A method that is widely used to determine the presence of oscillations in an electric power system is Prony analysis. Prony analysis is a powerful signal processing technique to estimate the parameters of a signal. In power systems, this is applied to estimate the parameters of power system signals, such as voltage, current, and frequency, which are essential for power system control and protection.</div><div>This research demonstrates Prony method to monitor oscillations in power system using synchrophasor data, establishing the correlation between oscillation at various busses within the grid and the implication of practical DSF value selection to the model accuracy for selected grid system. A modified Kundur’s four-machine two-area test system model is employed as base model, scaled to the actual measurement data of 45 Extra High Voltage Substation 500 kV in Indonesia power system interconnection. This paper discusses the principles of Prony analysis, its advantages and limitations, including the applications to detect power system oscillation. The performance of Prony analysis is evaluated by comparing the simulation data and real time measurement data from actual Java interconnection system. In practical applications within the selected grid, the DSF to mismatch the actual data and signal construction were then selected and matched as the reference for a particular grid application. The results demonstrate that Prony analysis, when applied to PMU data with a DSF value of 6, provides a reliable and effective method for detecting power system oscillations where the squared errors are reduced from 600–8000% with DSF=2–4 down to 0.03–10% for dominant oscillation","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101076"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711721","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 penetration of renewable and distributed energy resources presents significant challenges for voltage regulation in modern power distribution systems. Traditional optimization-based controllers often fail to deliver reliable performance under real-time constraints and high system uncertainty. To address these issues, recent research has increasingly focused on Multi-Agent Reinforcement Learning (MARL) algorithms to coordinate control units across distributed grid regions. While MARL offers strong potential, its performance can be limited by over-generalization and insufficient adaptability to diverse operating conditions. In this study, we propose a novel hybrid framework that integrates MARL with Particle Swarm Optimization (PSO) to address these challenges. Our method combines MARL’s ability to learn from experience with PSO’s efficient search capabilities to enhance voltage control across distributed networks. The proposed algorithm is evaluated on the MAPDN platform under 33-bus and 322-bus scenarios using four MARL variants. Experimental results demonstrate that the hybrid method achieves up to 10x reduction in power loss and consistently maintains a perfect control rate of 1.0, significantly outperforming standalone MARL approaches. The framework is scalable, adaptable to various MARL models and metaheuristic algorithms, and offers promising implications for data-driven voltage regulation in renewable-heavy smart grids.
{"title":"A hybrid multi-agent deep actor-critic learning and particle swarm optimization algorithm for active voltage control in smart grids with renewable energies","authors":"Elham Nazari , Negar Nazari , Samaneh Hosseini Semnani , Mohammad Reza Ahmadzadeh","doi":"10.1016/j.prime.2025.101075","DOIUrl":"10.1016/j.prime.2025.101075","url":null,"abstract":"<div><div>The increasing penetration of renewable and distributed energy resources presents significant challenges for voltage regulation in modern power distribution systems. Traditional optimization-based controllers often fail to deliver reliable performance under real-time constraints and high system uncertainty. To address these issues, recent research has increasingly focused on Multi-Agent Reinforcement Learning (MARL) algorithms to coordinate control units across distributed grid regions. While MARL offers strong potential, its performance can be limited by over-generalization and insufficient adaptability to diverse operating conditions. In this study, we propose a novel hybrid framework that integrates MARL with Particle Swarm Optimization (PSO) to address these challenges. Our method combines MARL’s ability to learn from experience with PSO’s efficient search capabilities to enhance voltage control across distributed networks. The proposed algorithm is evaluated on the MAPDN platform under 33-bus and 322-bus scenarios using four MARL variants. Experimental results demonstrate that the hybrid method achieves up to 10x reduction in power loss and consistently maintains a perfect control rate of 1.0, significantly outperforming standalone MARL approaches. The framework is scalable, adaptable to various MARL models and metaheuristic algorithms, and offers promising implications for data-driven voltage regulation in renewable-heavy smart grids.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101075"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711719","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 : 2025-07-15DOI: 10.1016/j.prime.2025.101074
Nadjet Zioui , Aicha Mahmoudi , Mehdi Fazilat , Oumar Kone , Reda Dermouche , Mohamed Tadjine
The need for effective control strategies in electrical motor drives has resulted in significant advances in inverter modulation approaches, particularly for permanent magnet synchronous machines (PMSMs). This study proposes a quantum-based strategy to improve energy efficiency, control precision, and system stability by developing quantum space vector pulse width modulation (QSVPWM) as an alternative technique to classical SVPWM for PMSM control. The proposed QSVPWM employs a quantum comparator implemented via a quantum subtractor for real numbers ranging from −100 % to +100 %. Trigonometric properties and the tensor product are combined to create a quantum sign function. The QSVPWM controller also incorporates quantum versions of classical logical gates such as and OR. The effectiveness of QSVPWM was assessed using MATLAB Simulink simulations, and its performance was compared with that of SVPWM under the same conditions. QSVPWM outperforms SVPWM in terms of control precision, oscillation reduction, and energy efficiency, reducing the root mean square speed error by 0.47 %, the d-axis current by 0.11 %, and the q-axis current by 0.59 %. Furthermore, a total harmonic distortion study revealed that QSVPWM reduces higher-order harmonics, thereby improving power quality and lowering energy losses. These enhancements help smooth control dynamics, minimize mechanical stress on components, and improve energy efficiency. In summary, QSVPWM outperforms traditional SVPWM, particularly for applications requiring precise control and greater energy savings in motor control systems.
{"title":"Quantum space vector pulse width modulation for speed control of permanent magnet synchronous machines","authors":"Nadjet Zioui , Aicha Mahmoudi , Mehdi Fazilat , Oumar Kone , Reda Dermouche , Mohamed Tadjine","doi":"10.1016/j.prime.2025.101074","DOIUrl":"10.1016/j.prime.2025.101074","url":null,"abstract":"<div><div>The need for effective control strategies in electrical motor drives has resulted in significant advances in inverter modulation approaches, particularly for permanent magnet synchronous machines (PMSMs). This study proposes a quantum-based strategy to improve energy efficiency, control precision, and system stability by developing quantum space vector pulse width modulation (QSVPWM) as an alternative technique to classical SVPWM for PMSM control. The proposed QSVPWM employs a quantum comparator implemented via a quantum subtractor for real numbers ranging from −100 % to +100 %. Trigonometric properties and the tensor product are combined to create a quantum sign function. The QSVPWM controller also incorporates quantum versions of classical logical gates such as and OR. The effectiveness of QSVPWM was assessed using MATLAB Simulink simulations, and its performance was compared with that of SVPWM under the same conditions. QSVPWM outperforms SVPWM in terms of control precision, oscillation reduction, and energy efficiency, reducing the root mean square speed error by 0.47 %, the <span>d</span>-axis current by 0.11 %, and the q-axis current by 0.59 %. Furthermore, a total harmonic distortion study revealed that QSVPWM reduces higher-order harmonics, thereby improving power quality and lowering energy losses. These enhancements help smooth control dynamics, minimize mechanical stress on components, and improve energy efficiency. In summary, QSVPWM outperforms traditional SVPWM, particularly for applications requiring precise control and greater energy savings in motor control systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101074"},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662842","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 : 2025-07-12DOI: 10.1016/j.prime.2025.101057
G. Hemanth Kumar , Sivananda Lahari Reddy Elicherla , Sugandha Saxena , K. Ayyappa Swamy , Ashwini P. , U. Pavan Kumar
Smart grid technology adoption at a fast pace has created new security vulnerabilities to cache side-channel attacks (CSAs) which threaten both user privacy and grid stability through edge computing devices. The current centralized detection methods need complete raw data collection, which leads to privacy risks and scalability limitations. The proposed PPFL framework provides distributed CSA detection across smart meters through a privacy-preserving federated learning approach that avoids data sharing. The solution uses differential privacy with 1.0–5.0 to secure aggregation and a lightweight CNN-LSTM model, which results in 96.3% detection accuracy while maintaining data confidentiality. Real-world smart meter datasets from UK-DALE and REDD, together with simulation tests, show that the framework operates efficiently (2.1 s training latency/round), has minimal communication overhead (1.2 MB/round), and remains resistant to adversarial attacks (4.8% accuracy drop under evasion attempts). The proposed framework demonstrates linear scalability to 10,000+ devices while using 2.7 Wh energy per round, which makes it suitable for extensive smart grid implementations that follow GDPR and NIST cybersecurity standards.
{"title":"FL-DPCSA: Federated learning with differential privacy for cache side-channel attack detection in edge-based smart grids","authors":"G. Hemanth Kumar , Sivananda Lahari Reddy Elicherla , Sugandha Saxena , K. Ayyappa Swamy , Ashwini P. , U. Pavan Kumar","doi":"10.1016/j.prime.2025.101057","DOIUrl":"10.1016/j.prime.2025.101057","url":null,"abstract":"<div><div>Smart grid technology adoption at a fast pace has created new security vulnerabilities to cache side-channel attacks (CSAs) which threaten both user privacy and grid stability through edge computing devices. The current centralized detection methods need complete raw data collection, which leads to privacy risks and scalability limitations. The proposed PPFL framework provides distributed CSA detection across smart meters through a privacy-preserving federated learning approach that avoids data sharing. The solution uses differential privacy with <span><math><mrow><mi>ϵ</mi><mo>=</mo></mrow></math></span> 1.0–5.0 to secure aggregation and a lightweight CNN-LSTM model, which results in 96.3% detection accuracy while maintaining data confidentiality. Real-world smart meter datasets from UK-DALE and REDD, together with simulation tests, show that the framework operates efficiently (2.1 s training latency/round), has minimal communication overhead (1.2 MB/round), and remains resistant to adversarial attacks (4.8% accuracy drop under evasion attempts). The proposed framework demonstrates linear scalability to 10,000+ devices while using 2.7 Wh energy per round, which makes it suitable for extensive smart grid implementations that follow GDPR and NIST cybersecurity standards.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101057"},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634326","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 : 2025-07-12DOI: 10.1016/j.prime.2025.101072
Oleg V. Marchenko
Thermoelectric generators (TEG) are compact, reliable, noiseless and environmentally-friendly solid state heat engines that use electrons and holes as working fluid. In recent years they have attracted attention as sources of "green" energy, as they use waste heat released in various industrial processes.
Along with numerical methods, methods based on averaging temperature-dependent thermoelectric parameters are widely used for modeling thermoelectric systems. Heuristic procedures used for this fail to determine their applicability limits and accuracy in the general case. This work introduces a novel approach through the application, justification, and study of the formal averaging method with an emphasis on the accuracy.
This paper proposes the method of average parameters, which is based on the perturbation method considering the smallness of thermoelectric effects compared to the heat transferred by conduction. The accuracy of the average parameter method was assessed for low-, medium-, and high-temperature materials by comparing its results with the numerical solution and calculations by other methods. For the materials considered, the error of the average parameters method in power and efficiency does not exceed 1 %. It is shown that the problems of averaging thermoelectric parameters and compatibility are closely related. The findings revealed that internal losses in a leg of a thermoelement with temperature-dependent properties are proportional to the deviation of the figure of merit, determined through the average properties of materials, from its average integral value. Relationships were established for calculating the losses in a segmented leg of a thermoelement.
{"title":"Thermoelectric generators: average parameters method and compatibility","authors":"Oleg V. Marchenko","doi":"10.1016/j.prime.2025.101072","DOIUrl":"10.1016/j.prime.2025.101072","url":null,"abstract":"<div><div>Thermoelectric generators (TEG) are compact, reliable, noiseless and environmentally-friendly solid state heat engines that use electrons and holes as working fluid. In recent years they have attracted attention as sources of \"green\" energy, as they use waste heat released in various industrial processes.</div><div>Along with numerical methods, methods based on averaging temperature-dependent thermoelectric parameters are widely used for modeling thermoelectric systems. Heuristic procedures used for this fail to determine their applicability limits and accuracy in the general case. This work introduces a novel approach through the application, justification, and study of the formal averaging method with an emphasis on the accuracy.</div><div>This paper proposes the method of average parameters, which is based on the perturbation method considering the smallness of thermoelectric effects compared to the heat transferred by conduction. The accuracy of the average parameter method was assessed for low-, medium-, and high-temperature materials by comparing its results with the numerical solution and calculations by other methods. For the materials considered, the error of the average parameters method in power and efficiency does not exceed 1 %. It is shown that the problems of averaging thermoelectric parameters and compatibility are closely related. The findings revealed that internal losses in a leg of a thermoelement with temperature-dependent properties are proportional to the deviation of the figure of merit, determined through the average properties of materials, from its average integral value. Relationships were established for calculating the losses in a segmented leg of a thermoelement.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101072"},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662841","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 : 2025-07-11DOI: 10.1016/j.prime.2025.101073
Wijaya Yudha Atmaja , Filipe Faria da Silva , Sarjiya
Exceeding the hosting capacity limits of photovoltaic (PV) systems can disrupt distribution network performance, leading to voltage instability and undesirable voltage rises. As PV integration expands, there is a need for the stochastic enhancement of distribution networks to increase hosting capacity and reduce voltage deviation. Traditional deterministic approaches often fail to address the inherent uncertainties in distributed PV penetration and to effectively utilize the VAR compensation capabilities of PV inverters. To overcome these limitations, a unified stochastic framework that integrates VAR compensation functions is introduced. A key contribution of this study is the probabilistic candidate selection method, which considers all customers as potential PV adopters and thus reflects real-world uncertainties in VAR compensation. Another contribution is the use of an unsimplified distribution grid model, offering a realistic representation of network conditions. A final contribution is the development of time-series based indices that capture the dynamic impacts of VAR compensation under varying load demand and solar irradiance. The results demonstrate that this methodology provides a practical approach for the stochastic enhancement of distribution networks, increasing hosting capacity and reducing voltage deviation.
{"title":"Stochastic enhancement of distribution network to increase hosting capacity and reduce voltage deviation","authors":"Wijaya Yudha Atmaja , Filipe Faria da Silva , Sarjiya","doi":"10.1016/j.prime.2025.101073","DOIUrl":"10.1016/j.prime.2025.101073","url":null,"abstract":"<div><div>Exceeding the hosting capacity limits of photovoltaic (PV) systems can disrupt distribution network performance, leading to voltage instability and undesirable voltage rises. As PV integration expands, there is a need for the stochastic enhancement of distribution networks to increase hosting capacity and reduce voltage deviation. Traditional deterministic approaches often fail to address the inherent uncertainties in distributed PV penetration and to effectively utilize the VAR compensation capabilities of PV inverters. To overcome these limitations, a unified stochastic framework that integrates VAR compensation functions is introduced. A key contribution of this study is the probabilistic candidate selection method, which considers all customers as potential PV adopters and thus reflects real-world uncertainties in VAR compensation. Another contribution is the use of an unsimplified distribution grid model, offering a realistic representation of network conditions. A final contribution is the development of time-series based indices that capture the dynamic impacts of VAR compensation under varying load demand and solar irradiance. The results demonstrate that this methodology provides a practical approach for the stochastic enhancement of distribution networks, increasing hosting capacity and reducing voltage deviation.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101073"},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686858","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 : 2025-07-09DOI: 10.1016/j.prime.2025.101063
José G. Moreno, John Morales, Eduardo A. Orduña
Fault analysis on transmission lines is essential for maintaining electric power system performance. Identifying fault types and characteristics such as fault resistance and inception angle, can help operators determine appropriate mitigation actions. For over four decades, fault data recorded by various intelligent electronic devices have been used to plot oscillograms and phasor diagrams, which, under certain conditions, are not easily interpretable. Additionally, a bibliographic review reveals that several efforts, particularly those based on the Fourier transform and Wavelet transform, have been made for post-fault analysis. However, the performance of these mathematical tools heavily depends on the number of decomposition levels and the choice of wavelets, sine, and cosine bases. Since 2016, ultra-high-speed protection relays based on incremental quantities have been deployed in real-world applications. In this context, this paper introduces a novel post-fault analysis method based on principal component analysis and incremental quantities. The proposed methodology enables the identification of characteristic patterns of short-circuit signals, such as fault type, involved phases, inception angle, and fault resistance. The algorithm was tested using simulated signals from the ATP (Alternative Transients Program) software, and the results confirm that the methodology is robust and capable of addressing a wide range of fault characteristics.
{"title":"A novel methodology for post-fault analysis using incremental quantities and principal component analysis","authors":"José G. Moreno, John Morales, Eduardo A. Orduña","doi":"10.1016/j.prime.2025.101063","DOIUrl":"10.1016/j.prime.2025.101063","url":null,"abstract":"<div><div>Fault analysis on transmission lines is essential for maintaining electric power system performance. Identifying fault types and characteristics such as fault resistance and inception angle, can help operators determine appropriate mitigation actions. For over four decades, fault data recorded by various intelligent electronic devices have been used to plot oscillograms and phasor diagrams, which, under certain conditions, are not easily interpretable. Additionally, a bibliographic review reveals that several efforts, particularly those based on the Fourier transform and Wavelet transform, have been made for post-fault analysis. However, the performance of these mathematical tools heavily depends on the number of decomposition levels and the choice of wavelets, sine, and cosine bases. Since 2016, ultra-high-speed protection relays based on incremental quantities have been deployed in real-world applications. In this context, this paper introduces a novel post-fault analysis method based on principal component analysis and incremental quantities. The proposed methodology enables the identification of characteristic patterns of short-circuit signals, such as fault type, involved phases, inception angle, and fault resistance. The algorithm was tested using simulated signals from the ATP (Alternative Transients Program) software, and the results confirm that the methodology is robust and capable of addressing a wide range of fault characteristics.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101063"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679176","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 : 2025-07-09DOI: 10.1016/j.prime.2025.101064
Shyma S , Brinda R , Padmasuresh L
Electric Vehicles (Evs) have enlarged significant popularity owing to their environmental benefits and the enhancing demand for sustainable transportation. Integrating Renewable Energy Sources (RES) such as Photovoltaic (PV) systems for EV charging improves the sustainability and cost effectiveness of overall system. For improving overall performance of EV charging systems, an effectual continuous charging of EV batteries is crucial. Therefore, this paper introduces a novel topology to optimal EV charging by utilizing an integrated SEPICCuk converter with energy management system. The integrated SEPICCuk converter is developed for enhancing the voltage obtained from PV, which offers high voltage gain with reduced component count. Moreover, to control the converter operation, the Chaotic Dragonfly Optimization (CDO) based Proportional Integral (PI) controller is used, it provide the stable output voltage with better convergence speed and robustness. The battery system gets charged by the PV system for EV charging and the bidirectional converter is employed for both charging and discharging purpose as per the battery needs. Additionally, the excess energy obtained from the PV system after supplying enough power to the battery is deliver to the grid system through single Voltage Source Inverter (VSI). During unavailable power from PV, the grid flows in bidirectional for charging the battery, thus continuous power supply is fed to battery system. To validate the effectiveness of developed system it is executed in MATLAB/Simulink and comparative analysis is made over with traditional topologies for showing the prominence of proposed work. The outcomes illustrates that the proposed converter approach has high voltage gain ratio of 1:10, high efficiency of 97.42 % as well as minimized Total Harmonic Distortion (THD) value of 2.42 %. Thereby, this research contributes to the ongoing efforts in developing advanced charging solutions with energy management system.
{"title":"A high-efficiency PV-based EV charging system with SEPIC-Cuk converter and energy management","authors":"Shyma S , Brinda R , Padmasuresh L","doi":"10.1016/j.prime.2025.101064","DOIUrl":"10.1016/j.prime.2025.101064","url":null,"abstract":"<div><div>Electric Vehicles (Evs) have enlarged significant popularity owing to their environmental benefits and the enhancing demand for sustainable transportation. Integrating Renewable Energy Sources (RES) such as Photovoltaic (PV) systems for EV charging improves the sustainability and cost effectiveness of overall system. For improving overall performance of EV charging systems, an effectual continuous charging of EV batteries is crucial. Therefore, this paper introduces a novel topology to optimal EV charging by utilizing an integrated SEPIC<img>Cuk converter with energy management system. The integrated SEPIC<img>Cuk converter is developed for enhancing the voltage obtained from PV, which offers high voltage gain with reduced component count. Moreover, to control the converter operation, the Chaotic Dragonfly Optimization (CDO) based Proportional Integral (PI) controller is used, it provide the stable output voltage with better convergence speed and robustness. The battery system gets charged by the PV system for EV charging and the bidirectional converter is employed for both charging and discharging purpose as per the battery needs. Additionally, the excess energy obtained from the PV system after supplying enough power to the battery is deliver to the grid system through single Voltage Source Inverter (VSI). During unavailable power from PV, the grid flows in bidirectional for charging the battery, thus continuous power supply is fed to battery system. To validate the effectiveness of developed system it is executed in MATLAB/Simulink and comparative analysis is made over with traditional topologies for showing the prominence of proposed work. The outcomes illustrates that the proposed converter approach has high voltage gain ratio of 1:10, high efficiency of 97.42 % as well as minimized Total Harmonic Distortion (THD) value of 2.42 %. Thereby, this research contributes to the ongoing efforts in developing advanced charging solutions with energy management system.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101064"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662786","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 : 2025-07-08DOI: 10.1016/j.prime.2025.101065
Laila Fitriana , Aris Purwanto , Fudhail A Munir , Wei-Cheng Wang , Herman Saputro
The growing demand for portable electronic devices has highlighted the need for compact, efficient, and long-lasting power sources. Conventional batteries suffer from low energy densities, limiting device operation time. This study addresses this limitation by investigating cylindrical stepped micro-combustors for thermoelectric generator applications through combined numerical and experimental methods. Three combustor types (A, B, and C) with varying flame chamber geometries were analyzed, along with two construction materials: stainless steel and quartz glass. Computational fluid dynamics simulations were used to evaluate flame stability, combustion efficiency, and temperature distribution, while experiments validated the models and measured electrical power output. Combustor Type B showed the best flame stability, and Type C achieved the highest wall temperature, the backward-facing step and wire mesh were very useful in maintaining the flame stability. While the experiment shows Stainless steel outperformed quartz glass, yielding a maximum power output of 0.7422 W compared to 0.5418 W. However, the cylindrical geometry proved less effective than vortex combustors due to limited heat transfer surface area. This work contributes to the field by providing quantitative insights into the effects of combustor geometry and materials on Thermoelectric performance, guiding the design of next generation micropower systems.
{"title":"Optimization of cylindrical stepped micro-combustors for enhanced thermoelectric power generation: A comprehensive numerical and experimental study on wire mesh positioning and material selection","authors":"Laila Fitriana , Aris Purwanto , Fudhail A Munir , Wei-Cheng Wang , Herman Saputro","doi":"10.1016/j.prime.2025.101065","DOIUrl":"10.1016/j.prime.2025.101065","url":null,"abstract":"<div><div>The growing demand for portable electronic devices has highlighted the need for compact, efficient, and long-lasting power sources. Conventional batteries suffer from low energy densities, limiting device operation time. This study addresses this limitation by investigating cylindrical stepped micro-combustors for thermoelectric generator applications through combined numerical and experimental methods. Three combustor types (A, B, and C) with varying flame chamber geometries were analyzed, along with two construction materials: stainless steel and quartz glass. Computational fluid dynamics simulations were used to evaluate flame stability, combustion efficiency, and temperature distribution, while experiments validated the models and measured electrical power output. Combustor Type B showed the best flame stability, and Type C achieved the highest wall temperature, the backward-facing step and wire mesh were very useful in maintaining the flame stability. While the experiment shows Stainless steel outperformed quartz glass, yielding a maximum power output of 0.7422 W compared to 0.5418 W. However, the cylindrical geometry proved less effective than vortex combustors due to limited heat transfer surface area. This work contributes to the field by providing quantitative insights into the effects of combustor geometry and materials on Thermoelectric performance, guiding the design of next generation micropower systems.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101065"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632397","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 : 2025-07-07DOI: 10.1016/j.prime.2025.101067
Luka Herc , Luka Perković , Tomislav Pukšec , Neven Duić
This research presents a novel method for the statistical evaluation of the synthetic driving cycles for small-to-medium vehicles, based on the real driving cycles recorded with a GPS tracker with a resolution of five seconds. The recorded data is processed so it can be used as input for energy planning, namely the estimation of battery electric vehicles' energy demand and charging strategies in the dump, smart and V2G regimes. Initial statistical analysis shows that hourly distribution among various vehicles is best represented with gamma distribution. However, due to the lower amount of data recorded from the GPS, synthetic driving cycles match the data measurement with a correlation of 0,5 and 0,8 for workdays and weekends, respectively. This drawback can be avoided with more data being recorded during the research on the topic and consequent re-tuning of the distribution parameters. Also, the variations in the process are presented with the use of different combinations of statistical distributions and machine learning.
{"title":"Modelling decarbonisation of the transport sector with method for assessing vehicle driving cycles based on real GPS data","authors":"Luka Herc , Luka Perković , Tomislav Pukšec , Neven Duić","doi":"10.1016/j.prime.2025.101067","DOIUrl":"10.1016/j.prime.2025.101067","url":null,"abstract":"<div><div>This research presents a novel method for the statistical evaluation of the synthetic driving cycles for small-to-medium vehicles, based on the real driving cycles recorded with a GPS tracker with a resolution of five seconds. The recorded data is processed so it can be used as input for energy planning, namely the estimation of battery electric vehicles' energy demand and charging strategies in the dump, smart and V2G regimes. Initial statistical analysis shows that hourly distribution among various vehicles is best represented with gamma distribution. However, due to the lower amount of data recorded from the GPS, synthetic driving cycles match the data measurement with a correlation of 0,5 and 0,8 for workdays and weekends, respectively. This drawback can be avoided with more data being recorded during the research on the topic and consequent re-tuning of the distribution parameters. Also, the variations in the process are presented with the use of different combinations of statistical distributions and machine learning.</div></div>","PeriodicalId":100488,"journal":{"name":"e-Prime - Advances in Electrical Engineering, Electronics and Energy","volume":"13 ","pages":"Article 101067"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605022","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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