Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey
The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/16/4776 |
| _version_ | 1827685385096921088 |
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| author | Seyed Mahdi Miraftabzadeh Michela Longo Federica Foiadelli Marco Pasetti Raul Igual |
| author_facet | Seyed Mahdi Miraftabzadeh Michela Longo Federica Foiadelli Marco Pasetti Raul Igual |
| author_sort | Seyed Mahdi Miraftabzadeh |
| collection | DOAJ |
| description | The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models. |
| first_indexed | 2024-03-10T08:52:01Z |
| format | Article |
| id | doaj.art-c5db70a8e4744de59400996611a12641 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:52:01Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c5db70a8e4744de59400996611a126412023-11-22T07:27:14ZengMDPI AGEnergies1996-10732021-08-011416477610.3390/en14164776Advances in the Application of Machine Learning Techniques for Power System Analytics: A SurveySeyed Mahdi Miraftabzadeh0Michela Longo1Federica Foiadelli2Marco Pasetti3Raul Igual4Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, ItalyDepartment of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyEduQTech, Electrical Engineering Department, EUP Teruel, Universidad de Zaragoza, 44003 Teruel, SpainThe recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.https://www.mdpi.com/1996-1073/14/16/4776machine learningpower systemssmart gridspower flowspower qualityphotovoltaic |
| spellingShingle | Seyed Mahdi Miraftabzadeh Michela Longo Federica Foiadelli Marco Pasetti Raul Igual Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey Energies machine learning power systems smart grids power flows power quality photovoltaic |
| title | Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey |
| title_full | Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey |
| title_fullStr | Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey |
| title_full_unstemmed | Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey |
| title_short | Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey |
| title_sort | advances in the application of machine learning techniques for power system analytics a survey |
| topic | machine learning power systems smart grids power flows power quality photovoltaic |
| url | https://www.mdpi.com/1996-1073/14/16/4776 |
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