Comparison of Artificial Intelligence and Machine Learning Methods Used in Electric Power System Operation

Marcel Hallmann (), Robert Pietracho and Przemyslaw Komarnicki
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Marcel Hallmann: Institute for Electrical Engineering, University of Applied Science Magdeburg-Stendal, 39114 Magdeburg, Germany
Robert Pietracho: Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland
Przemyslaw Komarnicki: Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany

Energies, 2024, vol. 17, issue 11, 1-25

Abstract: The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper.

Keywords: electric power system; smart grid; artificial intelligence; machine learning; digitalization; sector coupling (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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