AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review
The integration of distributed generation (DG), renewable energy sources (RES), and power electronic converters into distribution systems (DSs) has introduced significant power quality (PQ) challenges, such as voltage fluctuations, harmonic distortions, and transients. These issues can undermine the...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10852279/ |
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| author | Mitra Nabian Dehaghani Tarmo Korotko Argo Rosin |
| author_facet | Mitra Nabian Dehaghani Tarmo Korotko Argo Rosin |
| author_sort | Mitra Nabian Dehaghani |
| collection | DOAJ |
| description | The integration of distributed generation (DG), renewable energy sources (RES), and power electronic converters into distribution systems (DSs) has introduced significant power quality (PQ) challenges, such as voltage fluctuations, harmonic distortions, and transients. These issues can undermine the reliability and stability of power systems, making it essential to address them to ensure a consistent and resilient power supply, especially as RES adoption continues to grow. While previous reviews have explored artificial intelligence (AI) applications for PQ management, most have been limited to specific AI techniques or targeted PQ problems, such as harmonics. This review, however, offers a comprehensive synthesis of AI-based approaches across a wide range of PQ applications, encompassing detection, classification, and improvement, while also considering the specific PQ issues addressed in each case. By adopting an integrated approach, this review identifies key research gaps, particularly the limited focus on leveraging AI to control power converters in RESs for PQ improvement, as most existing studies emphasize devices like active power filters, compensators, and conditioners. The review also evaluates the effectiveness of these AI methods in terms of accuracy and the extent of total harmonic distortion (THD) reduction. In addition, it provides novel insights that can help guide researchers, engineers, and industry professionals toward developing more adaptive, scalable, and robust PQ solutions. Finally, future research directions are proposed to advance AI-based PQ management, facilitating the integration of AI into diverse and evolving power systems. |
| format | Article |
| id | doaj-art-016a3f7112ff4bfcb951f710be7a32e6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-016a3f7112ff4bfcb951f710be7a32e62025-01-31T00:00:44ZengIEEEIEEE Access2169-35362025-01-0113183461836510.1109/ACCESS.2025.353370210852279AI Applications for Power Quality Issues in Distribution Systems: A Systematic ReviewMitra Nabian Dehaghani0https://orcid.org/0000-0002-9390-1339Tarmo Korotko1https://orcid.org/0000-0002-7368-1797Argo Rosin2https://orcid.org/0000-0002-6485-1037Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, EstoniaElectrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, EstoniaElectrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn, EstoniaThe integration of distributed generation (DG), renewable energy sources (RES), and power electronic converters into distribution systems (DSs) has introduced significant power quality (PQ) challenges, such as voltage fluctuations, harmonic distortions, and transients. These issues can undermine the reliability and stability of power systems, making it essential to address them to ensure a consistent and resilient power supply, especially as RES adoption continues to grow. While previous reviews have explored artificial intelligence (AI) applications for PQ management, most have been limited to specific AI techniques or targeted PQ problems, such as harmonics. This review, however, offers a comprehensive synthesis of AI-based approaches across a wide range of PQ applications, encompassing detection, classification, and improvement, while also considering the specific PQ issues addressed in each case. By adopting an integrated approach, this review identifies key research gaps, particularly the limited focus on leveraging AI to control power converters in RESs for PQ improvement, as most existing studies emphasize devices like active power filters, compensators, and conditioners. The review also evaluates the effectiveness of these AI methods in terms of accuracy and the extent of total harmonic distortion (THD) reduction. In addition, it provides novel insights that can help guide researchers, engineers, and industry professionals toward developing more adaptive, scalable, and robust PQ solutions. Finally, future research directions are proposed to advance AI-based PQ management, facilitating the integration of AI into diverse and evolving power systems.https://ieeexplore.ieee.org/document/10852279/Artificial intelligencedistribution systemmicrogridspower qualityrenewable energy resources |
| spellingShingle | Mitra Nabian Dehaghani Tarmo Korotko Argo Rosin AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review IEEE Access Artificial intelligence distribution system microgrids power quality renewable energy resources |
| title | AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review |
| title_full | AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review |
| title_fullStr | AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review |
| title_full_unstemmed | AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review |
| title_short | AI Applications for Power Quality Issues in Distribution Systems: A Systematic Review |
| title_sort | ai applications for power quality issues in distribution systems a systematic review |
| topic | Artificial intelligence distribution system microgrids power quality renewable energy resources |
| url | https://ieeexplore.ieee.org/document/10852279/ |
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