Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role
This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies,...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10988820/ |
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| author | Chiranjeevi Yarramsetty Tukaram Moger Debashisha Jena Veeranki Srinivasa Rao |
| author_facet | Chiranjeevi Yarramsetty Tukaram Moger Debashisha Jena Veeranki Srinivasa Rao |
| author_sort | Chiranjeevi Yarramsetty |
| collection | DOAJ |
| description | This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems. |
| format | Article |
| id | doaj-art-58f8d51406044db99eb499ad5e2bf7a5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-58f8d51406044db99eb499ad5e2bf7a52025-08-20T03:09:59ZengIEEEIEEE Access2169-35362025-01-0113808718088810.1109/ACCESS.2025.356746410988820Advances in Composite Power System Reliability Assessment: Trends and Machine Learning RoleChiranjeevi Yarramsetty0https://orcid.org/0000-0002-3833-1644Tukaram Moger1https://orcid.org/0000-0003-4176-5125Debashisha Jena2https://orcid.org/0000-0001-8800-4652Veeranki Srinivasa Rao3https://orcid.org/0000-0001-9202-9633Department of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, IndiaDepartment of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, IndiaDepartment of Electrical and Electronics Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, IndiaDepartment of Electrical and Electronics Engineering, Aditya University, Surampalem, Kakinada, IndiaThis paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems.https://ieeexplore.ieee.org/document/10988820/Power system adequacyMonte Carlo simulationartificial intelligencerenewable integrationcomputational efficiency |
| spellingShingle | Chiranjeevi Yarramsetty Tukaram Moger Debashisha Jena Veeranki Srinivasa Rao Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role IEEE Access Power system adequacy Monte Carlo simulation artificial intelligence renewable integration computational efficiency |
| title | Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role |
| title_full | Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role |
| title_fullStr | Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role |
| title_full_unstemmed | Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role |
| title_short | Advances in Composite Power System Reliability Assessment: Trends and Machine Learning Role |
| title_sort | advances in composite power system reliability assessment trends and machine learning role |
| topic | Power system adequacy Monte Carlo simulation artificial intelligence renewable integration computational efficiency |
| url | https://ieeexplore.ieee.org/document/10988820/ |
| work_keys_str_mv | AT chiranjeeviyarramsetty advancesincompositepowersystemreliabilityassessmenttrendsandmachinelearningrole AT tukarammoger advancesincompositepowersystemreliabilityassessmenttrendsandmachinelearningrole AT debashishajena advancesincompositepowersystemreliabilityassessmenttrendsandmachinelearningrole AT veerankisrinivasarao advancesincompositepowersystemreliabilityassessmenttrendsandmachinelearningrole |