Pub Date : 2026-01-21DOI: 10.1109/TIA.2025.3613046
{"title":"IEEE Transactions on Industry Applications Publication Information","authors":"","doi":"10.1109/TIA.2025.3613046","DOIUrl":"https://doi.org/10.1109/TIA.2025.3613046","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 1","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11360531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-27DOI: 10.1109/TIA.2025.3625884
Sijia Yu;Yifan Zhou
Networked microgrids (NMGs) with low-inertia inverter-based resources (IBRs) require effective transient stability assessment (TSA) approaches to efficiently capture complex system-wide dynamics while preserving the data privacy of local microgrids. This paper developed a Quantum distributed Transient Stability Assessment via Pooling-Assisted Noise aggregation-Enhanced learning (QdTSA-PANE) method, where a quantum federated learning (QFL) framework and training algorithm are designed to leverage the expressibility of quantum operators while simultaneously handling quantum hardware noise and classical measurement noise. QdTSA-PANE introduces three main innovations: (1) a quantum noise aggregation mechanism for characterizing the influence of quantum noise throughout the quantum circuit; (2) a pooling-augmented vertical QFL architecture, with a theoretical proof of its robustness against quantum noise; and (3) a noise-aware learning algorithm that incorporates noise injection during training to improve generalization and enhance robustness under both quantum and classical noises. We validate QdTSA-PANE through extensive experiments on a typical NMG with IBRs. Results show a 46% improvement in test accuracy under realistic noises.
{"title":"Noise-Aggregation–Enhanced Quantum Federated Learning for Transient Stability Assessment of Networked Microgrids","authors":"Sijia Yu;Yifan Zhou","doi":"10.1109/TIA.2025.3625884","DOIUrl":"https://doi.org/10.1109/TIA.2025.3625884","url":null,"abstract":"Networked microgrids (NMGs) with low-inertia inverter-based resources (IBRs) require effective transient stability assessment (TSA) approaches to efficiently capture complex system-wide dynamics while preserving the data privacy of local microgrids. This paper developed a Quantum distributed Transient Stability Assessment via Pooling-Assisted Noise aggregation-Enhanced learning (QdTSA-PANE) method, where a quantum federated learning (QFL) framework and training algorithm are designed to leverage the expressibility of quantum operators while simultaneously handling quantum hardware noise and classical measurement noise. QdTSA-PANE introduces three main innovations: (1) a quantum noise aggregation mechanism for characterizing the influence of quantum noise throughout the quantum circuit; (2) a pooling-augmented vertical QFL architecture, with a theoretical proof of its robustness against quantum noise; and (3) a noise-aware learning algorithm that incorporates noise injection during training to improve generalization and enhance robustness under both quantum and classical noises. We validate QdTSA-PANE through extensive experiments on a typical NMG with IBRs. Results show a 46% improvement in test accuracy under realistic noises.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 2","pages":"3760-3772"},"PeriodicalIF":4.5,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1109/TIA.2025.3618624
Walter Aguiar Martins;Edson Martinho;Lia Hanna Martins Morita;Hédio Tatizawa;Evandro Aparecido Soares da Silva;Danilo Ferreira de Souza
In 2024, the Brazilian Association for Electrical Hazard Awareness documented 2,282 electrical accidents nationally, involving arc faults, direct-contact electrocutions, and 809 fatalities, with most incidents attributed to non-compliant electrical installations. Prior scholarly assessments of commercial building electrical systems in Brazil remain absent; this study addresses this gap through technical inspections and structured interviews across 494 urban commercial buildings in multiple states. Findings revealed 65% lacked certified electrical designs, over 50% utilized unqualified personnel, and 43.6% exhibited non-functional grounding systems. The absence of Ground Fault Circuit Interrupters (GFCIs) and Surge Protection Devices (SPDs) significantly elevated risks of lethal electrocution and equipment failure. Methodological rigor involved evaluating compliance with safety protocols and identifying systemic deviations. Results underscore critical non-conformities in Brazilian commercial infrastructures, necessitating immediate regulatory interventions, including mandatory design standardization, installer certification, and retrofitting SPD/GFCI infrastructure. This study advocates enhanced enforcement of electrical safety codes to mitigate risks and align installations with international standards (e.g., IEC 60364).
{"title":"Quality of Electrical Installations in Commercial Buildings in Brazil","authors":"Walter Aguiar Martins;Edson Martinho;Lia Hanna Martins Morita;Hédio Tatizawa;Evandro Aparecido Soares da Silva;Danilo Ferreira de Souza","doi":"10.1109/TIA.2025.3618624","DOIUrl":"https://doi.org/10.1109/TIA.2025.3618624","url":null,"abstract":"In 2024, the Brazilian Association for Electrical Hazard Awareness documented 2,282 electrical accidents nationally, involving arc faults, direct-contact electrocutions, and 809 fatalities, with most incidents attributed to non-compliant electrical installations. Prior scholarly assessments of commercial building electrical systems in Brazil remain absent; this study addresses this gap through technical inspections and structured interviews across 494 urban commercial buildings in multiple states. Findings revealed 65% lacked certified electrical designs, over 50% utilized unqualified personnel, and 43.6% exhibited non-functional grounding systems. The absence of Ground Fault Circuit Interrupters (GFCIs) and Surge Protection Devices (SPDs) significantly elevated risks of lethal electrocution and equipment failure. Methodological rigor involved evaluating compliance with safety protocols and identifying systemic deviations. Results underscore critical non-conformities in Brazilian commercial infrastructures, necessitating immediate regulatory interventions, including mandatory design standardization, installer certification, and retrofitting SPD/GFCI infrastructure. This study advocates enhanced enforcement of electrical safety codes to mitigate risks and align installations with international standards (e.g., IEC 60364).","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 2","pages":"2450-2457"},"PeriodicalIF":4.5,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1109/TIA.2025.3619920
Piyush P. Gajbhiye;Pradyumn Chaturvedi
Integration of DC sources and DC loads requires bidirectional DC-DC power electronic converters because of their ability to facilitate two-way power transfer between various voltage levels. Bidirectional DC-DC converters are essential components of contemporary power systems. This paper presents a generalized design of a bidirectional DC-DC resonant converter for buck operation in forward conduction and boost operation in reverse conduction. LC tank circuit is used for forward conduction and the same LC tank and LCC tank circuits are considered for reverse conduction operation. Minimizing the peak tank current and voltages of the tank circuit along with a lower reactive-to-active power ratio and achieving soft switching of all devices for the entire loading range are the design objectives of this paper. The analytical and simulation results are verified with a 200 W experimental prototype verifying the design. The maximum experimental efficiency above 96% is observed for LC forward buck conduction, LC reverse boost conduction and LCC reverse boost conduction operations.
{"title":"Design of Bidirectional DC-DC LC/LC/LCC Converter to Minimize Peak Tank Current and Peak Voltage Across Tank Circuit","authors":"Piyush P. Gajbhiye;Pradyumn Chaturvedi","doi":"10.1109/TIA.2025.3619920","DOIUrl":"https://doi.org/10.1109/TIA.2025.3619920","url":null,"abstract":"Integration of DC sources and DC loads requires bidirectional DC-DC power electronic converters because of their ability to facilitate two-way power transfer between various voltage levels. Bidirectional DC-DC converters are essential components of contemporary power systems. This paper presents a generalized design of a bidirectional DC-DC resonant converter for buck operation in forward conduction and boost operation in reverse conduction. LC tank circuit is used for forward conduction and the same LC tank and LCC tank circuits are considered for reverse conduction operation. Minimizing the peak tank current and voltages of the tank circuit along with a lower reactive-to-active power ratio and achieving soft switching of all devices for the entire loading range are the design objectives of this paper. The analytical and simulation results are verified with a 200 W experimental prototype verifying the design. The maximum experimental efficiency above 96% is observed for LC forward buck conduction, LC reverse boost conduction and LCC reverse boost conduction operations.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 1","pages":"1736-1746"},"PeriodicalIF":4.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-07DOI: 10.1109/TIA.2025.3618791
He Wang;Jinling Li;Xiao Liu
The deep reinforcement learning (DRL) approach with its end-to-end and data-driven features enhances the operational strategies for networked microgrids (NMGs). Well-trained DRL agents can make optimal decisions for system operations by observing NMG online measurements. However, missing measurements pose an unpredictable challenge for the secure operation of DRL-based NMGs. This paper proposes a large language model (LLM)-based agent designed for compatibility with multi-agent DRL online decision-making for networked microgrids considering missing measurements. The novel design rules for the LLM-based agent are proposed to unleash the potential of LLM in imputing missing NMG measurements using only few-shot learning. Subsequently, the designed LLM-based agent is embedded into multi-agent DRL in a compatible manner for NMG online operations considering missing online measurements. Experimental results indicate that the proposed method exhibits robustness and trustworthiness in device and system-level measurement imputations while holding the merits of DRL. The proposed method reduces NMG’s total operation costs by up to 23.33%, achieving a balance between security and optimality under missing measurements.
{"title":"Large Language Model Compatibility With Reinforcement Learning for Networked Microgrids Considering Device and System-Level Missing Measurements","authors":"He Wang;Jinling Li;Xiao Liu","doi":"10.1109/TIA.2025.3618791","DOIUrl":"https://doi.org/10.1109/TIA.2025.3618791","url":null,"abstract":"The deep reinforcement learning (DRL) approach with its end-to-end and data-driven features enhances the operational strategies for networked microgrids (NMGs). Well-trained DRL agents can make optimal decisions for system operations by observing NMG online measurements. However, missing measurements pose an unpredictable challenge for the secure operation of DRL-based NMGs. This paper proposes a large language model (LLM)-based agent designed for compatibility with multi-agent DRL online decision-making for networked microgrids considering missing measurements. The novel design rules for the LLM-based agent are proposed to unleash the potential of LLM in imputing missing NMG measurements using only few-shot learning. Subsequently, the designed LLM-based agent is embedded into multi-agent DRL in a compatible manner for NMG online operations considering missing online measurements. Experimental results indicate that the proposed method exhibits robustness and trustworthiness in device and system-level measurement imputations while holding the merits of DRL. The proposed method reduces NMG’s total operation costs by up to 23.33%, achieving a balance between security and optimality under missing measurements.","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"62 2","pages":"3746-3759"},"PeriodicalIF":4.5,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIA.2025.3610195
{"title":"Get Published in the New IEEE Open Journal of Industry Applications","authors":"","doi":"10.1109/TIA.2025.3610195","DOIUrl":"https://doi.org/10.1109/TIA.2025.3610195","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 6","pages":"8999-9000"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11174026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIA.2025.3607573
{"title":"IEEE Transactions on Industry Applications Information for Authors","authors":"","doi":"10.1109/TIA.2025.3607573","DOIUrl":"https://doi.org/10.1109/TIA.2025.3607573","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 6","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11174030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIA.2025.3607571
{"title":"IEEE Transactions on Industry Applications Publication Information","authors":"","doi":"10.1109/TIA.2025.3607571","DOIUrl":"https://doi.org/10.1109/TIA.2025.3607571","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 6","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11174027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIA.2025.3611041
{"title":"IEEE Women in Engineering","authors":"","doi":"10.1109/TIA.2025.3611041","DOIUrl":"https://doi.org/10.1109/TIA.2025.3611041","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 6","pages":"9716-9716"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173876","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/TIA.2025.3599752
{"title":"IEEE Industry Applications Society Information","authors":"","doi":"10.1109/TIA.2025.3599752","DOIUrl":"https://doi.org/10.1109/TIA.2025.3599752","url":null,"abstract":"","PeriodicalId":13337,"journal":{"name":"IEEE Transactions on Industry Applications","volume":"61 6","pages":"C2-C2"},"PeriodicalIF":4.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11173875","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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