Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review
Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing tech...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/1/37 |
| _version_ | 1797343423188959232 |
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| author | Fariha Imam Petr Musilek Marek Z. Reformat |
| author_facet | Fariha Imam Petr Musilek Marek Z. Reformat |
| author_sort | Fariha Imam |
| collection | DOAJ |
| description | Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems. |
| first_indexed | 2024-03-08T10:47:21Z |
| format | Article |
| id | doaj.art-903d02bf27384294b5b47b947370b3d7 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-08T10:47:21Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-903d02bf27384294b5b47b947370b3d72024-01-26T17:03:45ZengMDPI AGInformation2078-24892024-01-011513710.3390/info15010037Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A ReviewFariha Imam0Petr Musilek1Marek Z. Reformat2Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDepartment of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, CanadaDue to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.https://www.mdpi.com/2078-2489/15/1/37machine learningartificial intelligenceparametric modelnonparametric modelpower systempredictive maintenance |
| spellingShingle | Fariha Imam Petr Musilek Marek Z. Reformat Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review Information machine learning artificial intelligence parametric model nonparametric model power system predictive maintenance |
| title | Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review |
| title_full | Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review |
| title_fullStr | Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review |
| title_full_unstemmed | Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review |
| title_short | Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review |
| title_sort | parametric and nonparametric machine learning techniques for increasing power system reliability a review |
| topic | machine learning artificial intelligence parametric model nonparametric model power system predictive maintenance |
| url | https://www.mdpi.com/2078-2489/15/1/37 |
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