Pub Date : 2026-03-02DOI: 10.1109/OJIES.2026.3668543
Hafte Hayelom Adhena;Peyman Koohi;Alan J. Watson;Niek Moonen;Steve Greedy;Frank Leferink
The modular multiactive bridge (MMAB) DC–DC converter is increasingly considered for multiport power conversion; however, its conducted electromagnetic interference (EMI) behavior has received limited attention. In particular, the presence of multiple switching bridges, transformer parasitic capacitances, and high $dV/dt$ transitions can introduce significant common mode (CM) currents. This article investigates the conducted CM EMI characteristics of MMAB systems, with emphasis on the influence of topology selection and transformer parasitic elements. The effects of transformer leakage inductance, winding parasitic capacitances, and port configuration on CM emissions are analyzed using experimentally validated time- and frequency-domain measurements. Several MMAB configurations, including the fundamental topology and a hardware-level decoupled variant, are evaluated and compared. The results demonstrate that the conducted CM emission is strongly dependent on MMAB topology, phase-shift operation, leakage inductance, and winding parasitic capacitances. While hardware-level decoupling effectively mitigates power cross-coupling, it is shown to increase conducted CM emission.
{"title":"Conducted Common Mode Emission Analysis of a Three-Port Modular Multiactive Bridge System","authors":"Hafte Hayelom Adhena;Peyman Koohi;Alan J. Watson;Niek Moonen;Steve Greedy;Frank Leferink","doi":"10.1109/OJIES.2026.3668543","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3668543","url":null,"abstract":"The modular multiactive bridge (MMAB) DC–DC converter is increasingly considered for multiport power conversion; however, its conducted electromagnetic interference (EMI) behavior has received limited attention. In particular, the presence of multiple switching bridges, transformer parasitic capacitances, and high <inline-formula><tex-math>$dV/dt$</tex-math></inline-formula> transitions can introduce significant common mode (CM) currents. This article investigates the conducted CM EMI characteristics of MMAB systems, with emphasis on the influence of topology selection and transformer parasitic elements. The effects of transformer leakage inductance, winding parasitic capacitances, and port configuration on CM emissions are analyzed using experimentally validated time- and frequency-domain measurements. Several MMAB configurations, including the fundamental topology and a hardware-level decoupled variant, are evaluated and compared. The results demonstrate that the conducted CM emission is strongly dependent on MMAB topology, phase-shift operation, leakage inductance, and winding parasitic capacitances. While hardware-level decoupling effectively mitigates power cross-coupling, it is shown to increase conducted CM emission.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"513-524"},"PeriodicalIF":4.3,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11417720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents an advanced hybrid fault detection and diagnosis (FDD) framework for wind energy systems that integrates real sensor measurements with digital twin (DT) representations through hierarchical multisensor fusion, graph-based learning, and attention-driven ensemble modeling. The framework employs a global fusion mechanism to unify real and synthetic feature spaces, enhancing data diversity and enabling effective cross-domain knowledge transfer. A multimodal attention (MA) scheme further boosts discriminative power by combining deep latent embeddings with globally fused features, which are then processed through an optimized bagging ensemble to ensure robust generalization and computational efficiency. Extensive experiments on wind turbine datasets demonstrate the robustness and superiority of the proposed framework. The MA framework achieves perfect classification metrics across all evaluation criteria (accuracy, precision, recall, and F1-score), outperforming traditional and modern approaches, including random forest, k-nearest neighbors, decision tree, naive Bayes, support vector machine, and graph convolutional network models. These results underscore the effectiveness of attention-driven integration of sensor, DT, and relational features in delivering a scalable, high-performance, and interpretable FDD solution for contemporary wind energy systems.
{"title":"Attention-Driven Multisensor Fusion for Accurate and Real-Time Wind System Fault Diagnosis","authors":"Imen Nakti;Majdi Mansouri;Khadija Attouri;Atef Khedher;Mahamadou Abdou Tankari","doi":"10.1109/OJIES.2026.3667689","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3667689","url":null,"abstract":"This article presents an advanced hybrid fault detection and diagnosis (FDD) framework for wind energy systems that integrates real sensor measurements with digital twin (DT) representations through hierarchical multisensor fusion, graph-based learning, and attention-driven ensemble modeling. The framework employs a global fusion mechanism to unify real and synthetic feature spaces, enhancing data diversity and enabling effective cross-domain knowledge transfer. A multimodal attention (MA) scheme further boosts discriminative power by combining deep latent embeddings with globally fused features, which are then processed through an optimized bagging ensemble to ensure robust generalization and computational efficiency. Extensive experiments on wind turbine datasets demonstrate the robustness and superiority of the proposed framework. The MA framework achieves perfect classification metrics across all evaluation criteria (accuracy, precision, recall, and F1-score), outperforming traditional and modern approaches, including random forest, k-nearest neighbors, decision tree, naive Bayes, support vector machine, and graph convolutional network models. These results underscore the effectiveness of attention-driven integration of sensor, DT, and relational features in delivering a scalable, high-performance, and interpretable FDD solution for contemporary wind energy systems.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"502-512"},"PeriodicalIF":4.3,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11408852","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1109/OJIES.2026.3666294
Sathyaraj J;Sankardoss V
Precise forecasting of wind speed is essential for the efficient operation of wind power systems and the optimal planning of power grid generation. This study seeks to simplify the process of predicting short-term wind speeds, a crucial step for planning and conducting feasibility assessments of wind farms. In this article, a short-term wind speed forecasting model based on recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and hybrid deep learning and ensemble models is proposed, utilizing data from wind speed prediction in Turkey obtained by SCADA systems. These models use a range of activation functions, including hyperbolic tangent (tanh), inverse hyperbolic tangent (archtanh), complex hyperbolic tangent (hardtanh), exponential linear unit (ELU), Gaussian error linear unit (GELU), parametric exponential linear unit (PELU), scaled exponential linear unit (SELU), rectified linear unit (ReLU), leaky ReLU (Leaky ReLU), parametric ReLU (PReLU), randomized leaky ReLU (RReLU), and sigmoid-weighted linear unit (SWISH). These models were trained on time series data, with 75% of the data allocated for training and 25% for testing. The performance of deep learning, hybrid deep learning, and ensemble learning models is measured using a variety of metrics such as mean square error (MSE), normalized MSE, root mean square error (RMSE), normalized RMSE, relative RMSE, mean absolute error, symmetric mean absolute percentage error, mean bias error, and the coefficient of determination. The outcomes show that the ensemble learning models integrating RNN, LSTM, and GRU, utilizing RReLU and PReLU activation functions, are best at predicting wind speed, with a coefficient of determination ranging from 0.9684 to 0.9688.
{"title":"A Comparative Study on RNN-LSTM-GRU Ensemble Model for Short-Term Wind Speed Prediction","authors":"Sathyaraj J;Sankardoss V","doi":"10.1109/OJIES.2026.3666294","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3666294","url":null,"abstract":"Precise forecasting of wind speed is essential for the efficient operation of wind power systems and the optimal planning of power grid generation. This study seeks to simplify the process of predicting short-term wind speeds, a crucial step for planning and conducting feasibility assessments of wind farms. In this article, a short-term wind speed forecasting model based on recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and hybrid deep learning and ensemble models is proposed, utilizing data from wind speed prediction in Turkey obtained by SCADA systems. These models use a range of activation functions, including hyperbolic tangent (tanh), inverse hyperbolic tangent (archtanh), complex hyperbolic tangent (hardtanh), exponential linear unit (ELU), Gaussian error linear unit (GELU), parametric exponential linear unit (PELU), scaled exponential linear unit (SELU), rectified linear unit (ReLU), leaky ReLU (Leaky ReLU), parametric ReLU (PReLU), randomized leaky ReLU (RReLU), and sigmoid-weighted linear unit (SWISH). These models were trained on time series data, with 75% of the data allocated for training and 25% for testing. The performance of deep learning, hybrid deep learning, and ensemble learning models is measured using a variety of metrics such as mean square error (MSE), normalized MSE, root mean square error (RMSE), normalized RMSE, relative RMSE, mean absolute error, symmetric mean absolute percentage error, mean bias error, and the coefficient of determination. The outcomes show that the ensemble learning models integrating RNN, LSTM, and GRU, utilizing RReLU and PReLU activation functions, are best at predicting wind speed, with a coefficient of determination ranging from 0.9684 to 0.9688.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"488-501"},"PeriodicalIF":4.3,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11399856","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-19DOI: 10.1109/OJIES.2026.3666358
Nao Yoshimura;Hiroshi Oyama;Takuya Azumi
In recent years, embedded systems have become indispensable across a wide range of domains, including automotive and internet of things (IoT) applications. The increasing scale and complexity of these systems have intensified the demand in industrial development environments for platforms that ensure reliability, maintainability, and efficiency simultaneously. Conventional programming languages, such as C and C++, offer high performance but suffer from serious issues related to memory safety, resulting in bugs and security vulnerabilities. In contrast, Rust enables safe development by enforcing memory safety at compile time through its ownership and borrowing mechanisms, contributing to improved code quality and maintainability in industrial settings. Component-based development (CBD) supports modularization by units of functionality, enhancing reusability and structural clarity, which facilitates the handling of large-scale and complex industrial systems. Modern embedded applications, which require high processing performance and energy efficiency, increasingly adopt multiprocessor architectures to enable parallel execution. This study proposes a Rust-compatible framework that combines the safety features of Rust with the structural advantages of CBD, based on the TOPPERS Embedded Component Systems (TECSs) and the multiprocessor real-time operating system (RTOS) TOPPERS flexible multiprocessor profile 3 (FMP3). The proposed framework facilitates safe and reusable CBD in practice by designing and automatically generating Rust code that represents the TECS component structure and through optimization of exclusive control while maintaining safety. Evaluation of execution time demonstrates minimal overhead from integration with the multiprocessor RTOS, enabling efficient and reliable application development.
{"title":"FMP3+TECS/Rust: Memory-Safe Component Framework for Multiprocessor Embedded Systems","authors":"Nao Yoshimura;Hiroshi Oyama;Takuya Azumi","doi":"10.1109/OJIES.2026.3666358","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3666358","url":null,"abstract":"In recent years, embedded systems have become indispensable across a wide range of domains, including automotive and internet of things (IoT) applications. The increasing scale and complexity of these systems have intensified the demand in industrial development environments for platforms that ensure reliability, maintainability, and efficiency simultaneously. Conventional programming languages, such as C and C++, offer high performance but suffer from serious issues related to memory safety, resulting in bugs and security vulnerabilities. In contrast, Rust enables safe development by enforcing memory safety at compile time through its <monospace>ownership</monospace> and <monospace>borrowing</monospace> mechanisms, contributing to improved code quality and maintainability in industrial settings. Component-based development (CBD) supports modularization by units of functionality, enhancing reusability and structural clarity, which facilitates the handling of large-scale and complex industrial systems. Modern embedded applications, which require high processing performance and energy efficiency, increasingly adopt multiprocessor architectures to enable parallel execution. This study proposes a Rust-compatible framework that combines the safety features of Rust with the structural advantages of CBD, based on the TOPPERS Embedded Component Systems (TECSs) and the multiprocessor real-time operating system (RTOS) TOPPERS flexible multiprocessor profile 3 (FMP3). The proposed framework facilitates safe and reusable CBD in practice by designing and automatically generating Rust code that represents the TECS component structure and through optimization of exclusive control while maintaining safety. Evaluation of execution time demonstrates minimal overhead from integration with the multiprocessor RTOS, enabling efficient and reliable application development.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"469-487"},"PeriodicalIF":4.3,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11399902","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article introduces a parallel differential power processing (PDPP) architecture for photovoltaic (PV)/battery applications. The PV to Virtual Bus (PV2VB) architecture enables the integration of a battery and manages its power while performing maximum power point tracking on the PV strings. In the proposed PV2VB PDPP architecture, the battery is positioned at the virtual bus, acting as the input for all string-level converters (SLCs). By selecting a lower voltage for the battery at the virtual bus compared to the PV string or the main bus voltages, component voltage ratings can be reduced. The architecture employs dual active bridge converters connected to bridgeless (BL) converters as SLCs to generate both positive and negative output voltages while providing isolation. These SLCs track the maximum power point of each PV string, while the central converter manages battery charging and discharging. Experimental results confirm the performance and effectiveness of the proposed PV2VB PDPP architecture, achieving efficiencies between 95.5% and 99%.
{"title":"Integration of Battery Into Photovoltaic to Virtual Bus Parallel Differential Power Processing Architecture","authors":"Afshin Nazer;Olindo Isabella;Hani Vahedi;Patrizio Manganiello","doi":"10.1109/OJIES.2026.3664637","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3664637","url":null,"abstract":"This article introduces a parallel differential power processing (PDPP) architecture for photovoltaic (PV)/battery applications. The PV to Virtual Bus (PV2VB) architecture enables the integration of a battery and manages its power while performing maximum power point tracking on the PV strings. In the proposed PV2VB PDPP architecture, the battery is positioned at the virtual bus, acting as the input for all string-level converters (SLCs). By selecting a lower voltage for the battery at the virtual bus compared to the PV string or the main bus voltages, component voltage ratings can be reduced. The architecture employs dual active bridge converters connected to bridgeless (BL) converters as SLCs to generate both positive and negative output voltages while providing isolation. These SLCs track the maximum power point of each PV string, while the central converter manages battery charging and discharging. Experimental results confirm the performance and effectiveness of the proposed PV2VB PDPP architecture, achieving efficiencies between 95.5% and 99%.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"442-453"},"PeriodicalIF":4.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11395517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-13DOI: 10.1109/OJIES.2026.3664791
Mohammad Raeispour;Shuo Yan;Lasantha Meegahapola;Xinghuo Yu
Virtual power plants (VPPs) integrate distributed generation, storage systems, and load under a common control and market framework. Although the VPP concept brings improved flexibility and new services to the utility, it substantially endangers the grid due to the large cyber-physical attack surface. This article investigates the cyber-physical security of VPPs in modern power systems. First, the structure of VPPs is outlined, including main components, control hierarchies, communication layers, and ancillary services. Furthermore, the cyber and physical vulnerabilities are mapped across different layers of the VPP. This mapping further leads to a structured taxonomy of attacks common in VPP context. Following the vulnerability analysis, this article looks into the different defense mechanisms relevant to VPP-coordinated distributed energy systems, in categorization of prevention, detection and isolation, and resilient control. The comprehensive investigation highlights the pressing need for the development of robust co-designed security and resilience strategies across all layers of VPPs to safeguard their operations against evolving cyber-physical risk, which remains to be a significant gap in the VPP study.
{"title":"Cyber-Physical Security of Virtual Power Plants: A Survey","authors":"Mohammad Raeispour;Shuo Yan;Lasantha Meegahapola;Xinghuo Yu","doi":"10.1109/OJIES.2026.3664791","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3664791","url":null,"abstract":"Virtual power plants (VPPs) integrate distributed generation, storage systems, and load under a common control and market framework. Although the VPP concept brings improved flexibility and new services to the utility, it substantially endangers the grid due to the large cyber-physical attack surface. This article investigates the cyber-physical security of VPPs in modern power systems. First, the structure of VPPs is outlined, including main components, control hierarchies, communication layers, and ancillary services. Furthermore, the cyber and physical vulnerabilities are mapped across different layers of the VPP. This mapping further leads to a structured taxonomy of attacks common in VPP context. Following the vulnerability analysis, this article looks into the different defense mechanisms relevant to VPP-coordinated distributed energy systems, in categorization of prevention, detection and isolation, and resilient control. The comprehensive investigation highlights the pressing need for the development of robust co-designed security and resilience strategies across all layers of VPPs to safeguard their operations against evolving cyber-physical risk, which remains to be a significant gap in the VPP study.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"416-441"},"PeriodicalIF":4.3,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11396009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-12DOI: 10.1109/OJIES.2026.3664313
Mirella M. de O. Carneiro;Fernanda D. V. R. Oliveira;Victor R. R. de Oliveira;José Gabriel R. C. Gomes;Milena F. Pinto
This article presents a high dynamic range (HDR) CMOS image sensor, which is designed using a 0.18-$mu$m technology, resulting from integrating two previously studied HDR sensor architectures. In this image sensor, we use the readout circuit from a previous design about an HDR CMOS image sensor employing programmable linear–logarithmic counter for low-light imaging applications, combined with the pixel, which is slightly modified, from a previous work about a pixel for asynchronous HDR acquisition through adaptive tone mapping. The pixel resembles the design of a 3T pixel, but includes two more transistors (five in total) in order to allow for the use of the average photocurrent information from the entire pixel array. It is thus possible to obtain an image sensor with programmable sensitivity and HDR. The proposed pixel holds (considering a 10 $mu$m × 10 $mu$m photodiode) a fill factor of 65%, a pixel pitch of 16.93 $mu$m, and a pixel area of 15.12 $mu$m × 10.16 $mu$m. By configuring sensor parameters, it is possible to perform tone mapping and to control the image brightness, contrast, detail levels, and the number of bits used in the image. As a consequence, the resulting image may include either more or less detail, as defined by application-specific (or user-selected) parameters. The simulated dynamic range of the proposed image sensor is equal to 95.72 dB.
{"title":"Design and Simulation of an HDR CMOS Image Sensor Employing Programmable Linear–Logarithmic Counter With Nonlinear Response Pixel","authors":"Mirella M. de O. Carneiro;Fernanda D. V. R. Oliveira;Victor R. R. de Oliveira;José Gabriel R. C. Gomes;Milena F. Pinto","doi":"10.1109/OJIES.2026.3664313","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3664313","url":null,"abstract":"This article presents a high dynamic range (HDR) CMOS image sensor, which is designed using a 0.18-<inline-formula><tex-math>$mu$</tex-math></inline-formula>m technology, resulting from integrating two previously studied HDR sensor architectures. In this image sensor, we use the readout circuit from a previous design about an HDR CMOS image sensor employing programmable linear–logarithmic counter for low-light imaging applications, combined with the pixel, which is slightly modified, from a previous work about a pixel for asynchronous HDR acquisition through adaptive tone mapping. The pixel resembles the design of a 3T pixel, but includes two more transistors (five in total) in order to allow for the use of the average photocurrent information from the entire pixel array. It is thus possible to obtain an image sensor with programmable sensitivity and HDR. The proposed pixel holds (considering a 10 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m × 10 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m photodiode) a fill factor of 65%, a pixel pitch of 16.93 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m, and a pixel area of 15.12 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m × 10.16 <inline-formula><tex-math>$mu$</tex-math></inline-formula>m. By configuring sensor parameters, it is possible to perform tone mapping and to control the image brightness, contrast, detail levels, and the number of bits used in the image. As a consequence, the resulting image may include either more or less detail, as defined by application-specific (or user-selected) parameters. The simulated dynamic range of the proposed image sensor is equal to 95.72 dB.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"393-415"},"PeriodicalIF":4.3,"publicationDate":"2026-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11393936","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-11DOI: 10.1109/OJIES.2026.3663897
Oscar Lahuerta;Claudio Carretero;Luis Angel Barragan;Denis Navarro;Jesus Acero
This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.
{"title":"Hybrid-Timescale Physics-Informed Neural Network for Electrical Equivalent Impedance Identification in Induction Heating Systems","authors":"Oscar Lahuerta;Claudio Carretero;Luis Angel Barragan;Denis Navarro;Jesus Acero","doi":"10.1109/OJIES.2026.3663897","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3663897","url":null,"abstract":"This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"382-392"},"PeriodicalIF":4.3,"publicationDate":"2026-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11393584","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/OJIES.2026.3662751
Deniz Zargariafshar;Mehran Sabahi;Mohammad Bagher Bannae Sharifian;Ebrahim Babaei
In this article, an improved isolated single-phase buck-boost AC–AC converter with inherent commutation using only three switches is proposed. The proposed topology utilizes only one high-frequency (H-F) switch along with two line-frequency polarity changer switches to achieve both inverting and noninverting operation, which clearly distinguishes it from existing isolated AC–AC converters. Unlike converters that rely on positive/negative switching cells, the inherent commutation in the proposed structure ensures that all components remain active throughout both cycles of the voltage of input. As a result, input current remains continuous, snubber circuits and bidirectional H-F switches are eliminated and no dead times is applied, so the circuit structure, control and switching strategy are simplified. A comprehensive comparative analysis is conducted in terms of stresses of switch (voltage and current), peak and average switching device power indices, component count, power density, and efficiency. The results demonstrate that the proposed converter achieves lower losses and reduced volume, offering a compact, cost-effective, and lightweight solution. The operating principles are analyzed across different modes, and key relationships are summarized for clarity. A laboratory prototype operating at 100 Vrms confirms the theoretical analysis and validates the practical feasibility of the proposed converter.
{"title":"An Improved Isolated Single-Phase Buck-Boost AC–AC Converter With Inherent Commutation Using Only Three Switches","authors":"Deniz Zargariafshar;Mehran Sabahi;Mohammad Bagher Bannae Sharifian;Ebrahim Babaei","doi":"10.1109/OJIES.2026.3662751","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3662751","url":null,"abstract":"In this article, an improved isolated single-phase buck-boost AC–AC converter with inherent commutation using only three switches is proposed. The proposed topology utilizes only one high-frequency (H-F) switch along with two line-frequency polarity changer switches to achieve both inverting and noninverting operation, which clearly distinguishes it from existing isolated AC–AC converters. Unlike converters that rely on positive/negative switching cells, the inherent commutation in the proposed structure ensures that all components remain active throughout both cycles of the voltage of input. As a result, input current remains continuous, snubber circuits and bidirectional H-F switches are eliminated and no dead times is applied, so the circuit structure, control and switching strategy are simplified. A comprehensive comparative analysis is conducted in terms of stresses of switch (voltage and current), peak and average switching device power indices, component count, power density, and efficiency. The results demonstrate that the proposed converter achieves lower losses and reduced volume, offering a compact, cost-effective, and lightweight solution. The operating principles are analyzed across different modes, and key relationships are summarized for clarity. A laboratory prototype operating at 100 <italic>V</i><sub>rms</sub> confirms the theoretical analysis and validates the practical feasibility of the proposed converter.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"454-468"},"PeriodicalIF":4.3,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11386665","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1109/OJIES.2026.3660150
Yang Ma;Zhong Chen;Jiakai Gan;Lexuan Huang;Yaming Xu
The cascaded H-bridge (CHB) inverters has drawn much attention in renewable energy and industrial applications. Conventional modulation strategies for CHB inverters face challenges in simultaneously achieving optimal output voltage harmonic characteristics and active power balancing among cascaded units. To address this issue, this article proposes a novel power-balancing modulation strategy based on carrier cycle adjustment for nine-level CHB inverters with full modulation ratio range. The proposed method extends conventional phase disposition modulation by synchronously rearranging the carriers above and below the time axis, thereby creating a new carrier period cycle that can improve the output of internal power units. Within this framework, duty cycle expressions for each cascaded unit are derived, and the corresponding fundamental voltage components are analytically obtained. The analysis demonstrates that the proposed strategy achieves effective power balancing across all cascaded units within a single fundamental cycle. Finally, both simulation and experimental results confirm the effectiveness and practicality of the method.
{"title":"Module Power Balance Modulation Method for Cascaded Multilevel Inverters With Full Modulation Ratio Range","authors":"Yang Ma;Zhong Chen;Jiakai Gan;Lexuan Huang;Yaming Xu","doi":"10.1109/OJIES.2026.3660150","DOIUrl":"https://doi.org/10.1109/OJIES.2026.3660150","url":null,"abstract":"The cascaded H-bridge (CHB) inverters has drawn much attention in renewable energy and industrial applications. Conventional modulation strategies for CHB inverters face challenges in simultaneously achieving optimal output voltage harmonic characteristics and active power balancing among cascaded units. To address this issue, this article proposes a novel power-balancing modulation strategy based on carrier cycle adjustment for nine-level CHB inverters with full modulation ratio range. The proposed method extends conventional phase disposition modulation by synchronously rearranging the carriers above and below the time axis, thereby creating a new carrier period cycle that can improve the output of internal power units. Within this framework, duty cycle expressions for each cascaded unit are derived, and the corresponding fundamental voltage components are analytically obtained. The analysis demonstrates that the proposed strategy achieves effective power balancing across all cascaded units within a single fundamental cycle. Finally, both simulation and experimental results confirm the effectiveness and practicality of the method.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"7 ","pages":"369-381"},"PeriodicalIF":4.3,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11370549","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：