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

Author

Listed:
  • Marcel Hallmann

    (Institute for Electrical Engineering, University of Applied Science Magdeburg-Stendal, 39114 Magdeburg, Germany
    Fraunhofer Institute for Factory Operation and Automation IFF, 39106 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)

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.

Suggested Citation

  • Marcel Hallmann & Robert Pietracho & Przemyslaw Komarnicki, 2024. "Comparison of Artificial Intelligence and Machine Learning Methods Used in Electric Power System Operation," Energies, MDPI, vol. 17(11), pages 1-25, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2790-:d:1410067
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    References listed on IDEAS

    as
    1. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
    2. Christoph Wenge & Robert Pietracho & Stephan Balischewski & Bartlomiej Arendarski & Pio Lombardi & Przemyslaw Komarnicki & Leszek Kasprzyk, 2020. "Multi Usage Applications of Li-Ion Battery Storage in a Large Photovoltaic Plant: A Practical Experience," Energies, MDPI, vol. 13(18), pages 1-18, September.
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