Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights
Power Quality (PQ) disturbances are critical in modern power systems, significantly impacting electrical networks' stability, reliability, and efficiency. With the increasing penetration of renewable energy sources, non-linear loads, and power electronic devices, the detection, classification,...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024021169 |
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| author | Indu Sekhar Samanta Sarthak Mohanty Shubhranshu Mohan Parida Pravat Kumar Rout Subhasis Panda Mohit Bajaj Vojtech Blazek Lukas Prokop Stanislav Misak |
| author_facet | Indu Sekhar Samanta Sarthak Mohanty Shubhranshu Mohan Parida Pravat Kumar Rout Subhasis Panda Mohit Bajaj Vojtech Blazek Lukas Prokop Stanislav Misak |
| author_sort | Indu Sekhar Samanta |
| collection | DOAJ |
| description | Power Quality (PQ) disturbances are critical in modern power systems, significantly impacting electrical networks' stability, reliability, and efficiency. With the increasing penetration of renewable energy sources, non-linear loads, and power electronic devices, the detection, classification, and mitigation of PQ disturbances have become more complex. Traditional PQ analysis methods, which rely heavily on human expertise and rule-based systems, are often insufficient in handling the growing complexity and volume of data in real-time applications. This review comprehensively analyzes the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) techniques applied to PQ analysis, achieving classification accuracies as high as 99.94 % with hybrid approaches like dual-tree wavelet packet transforms combined with extreme learning machine (ELM). Integrating advanced signal processing techniques, such as wavelet transforms and empirical mode decomposition, has demonstrated accuracy improvements of up to 5 % in challenging scenarios. This paper explores the challenges associated with AI-based PQ analysis, including the need for large datasets, overfitting issues, and the lack of interpretability in complex models. Future research directions are outlined, emphasizing the development of hybrid models, explainable AI systems, and real-time adaptability to dynamic grid conditions. This review provides a holistic understanding of state-of-the-art AI/ML methods in PQ analysis. It highlights their potential to transform modern power systems by ensuring higher reliability, better fault detection, and more efficient power delivery. |
| format | Article |
| id | doaj-art-d549d97d049f4a339b088816c1dea3e1 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-d549d97d049f4a339b088816c1dea3e12025-01-06T04:08:55ZengElsevierResults in Engineering2590-12302025-03-0125103873Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insightsIndu Sekhar Samanta0Sarthak Mohanty1Shubhranshu Mohan Parida2Pravat Kumar Rout3Subhasis Panda4Mohit Bajaj5Vojtech Blazek6Lukas Prokop7Stanislav Misak8Department of Computer Science Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha 751030, IndiaDepartment of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha 751030, IndiaDepartment of Electrical Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha 751030, IndiaDepartment of Electrical and Electronics Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha 751030, IndiaDepartment of Electrical Engineering, Srinix College of Engineering, Odisha, IndiaDepartment of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan; College of Engineering, University of Business and Technology, Jeddah, 21448, Saudi Arabia; Corresponding author at: Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India.ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicPower Quality (PQ) disturbances are critical in modern power systems, significantly impacting electrical networks' stability, reliability, and efficiency. With the increasing penetration of renewable energy sources, non-linear loads, and power electronic devices, the detection, classification, and mitigation of PQ disturbances have become more complex. Traditional PQ analysis methods, which rely heavily on human expertise and rule-based systems, are often insufficient in handling the growing complexity and volume of data in real-time applications. This review comprehensively analyzes the latest advancements in Artificial Intelligence (AI) and Machine Learning (ML) techniques applied to PQ analysis, achieving classification accuracies as high as 99.94 % with hybrid approaches like dual-tree wavelet packet transforms combined with extreme learning machine (ELM). Integrating advanced signal processing techniques, such as wavelet transforms and empirical mode decomposition, has demonstrated accuracy improvements of up to 5 % in challenging scenarios. This paper explores the challenges associated with AI-based PQ analysis, including the need for large datasets, overfitting issues, and the lack of interpretability in complex models. Future research directions are outlined, emphasizing the development of hybrid models, explainable AI systems, and real-time adaptability to dynamic grid conditions. This review provides a holistic understanding of state-of-the-art AI/ML methods in PQ analysis. It highlights their potential to transform modern power systems by ensuring higher reliability, better fault detection, and more efficient power delivery.http://www.sciencedirect.com/science/article/pii/S2590123024021169Power quality disturbancesArtificial intelligenceMachine learningExtreme learning machineSupport vector machineFuzzy Expert Systems |
| spellingShingle | Indu Sekhar Samanta Sarthak Mohanty Shubhranshu Mohan Parida Pravat Kumar Rout Subhasis Panda Mohit Bajaj Vojtech Blazek Lukas Prokop Stanislav Misak Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights Results in Engineering Power quality disturbances Artificial intelligence Machine learning Extreme learning machine Support vector machine Fuzzy Expert Systems |
| title | Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights |
| title_full | Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights |
| title_fullStr | Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights |
| title_full_unstemmed | Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights |
| title_short | Artificial intelligence and machine learning techniques for power quality event classification: a focused review and future insights |
| title_sort | artificial intelligence and machine learning techniques for power quality event classification a focused review and future insights |
| topic | Power quality disturbances Artificial intelligence Machine learning Extreme learning machine Support vector machine Fuzzy Expert Systems |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024021169 |
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