Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Seyed Mahdi Miraftabzadeh, Michela Longo, Federica Foiadelli, Marco Pasetti and Raul Igual
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Seyed Mahdi Miraftabzadeh: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Michela Longo: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Federica Foiadelli: Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
Marco Pasetti: Department of Information Engineering, University of Brescia, Via Branze 38, 25123 Brescia, Italy
Raul Igual: EduQTech, Electrical Engineering Department, EUP Teruel, Universidad de Zaragoza, 44003 Teruel, Spain

Energies, 2021, vol. 14, issue 16, 1-24

Abstract: 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.

Keywords: machine learning; power systems; smart grids; power flows; power quality; photovoltaic; intelligent transportation; load forecasting; survey (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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