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A Brief Survey on the Development of Intelligent Dispatcher Training Simulators

Author

Listed:
  • Ao Dong

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xinyi Lai

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Chunlong Lin

    (Beijing Kedong Electric Control System Co., Ltd., Haidian District, Beijing 100192, China
    NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Changnian Lin

    (Beijing Kedong Electric Control System Co., Ltd., Haidian District, Beijing 100192, China
    NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Wei Jin

    (Beijing Kedong Electric Control System Co., Ltd., Haidian District, Beijing 100192, China
    NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Fushuan Wen

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research.

Suggested Citation

  • Ao Dong & Xinyi Lai & Chunlong Lin & Changnian Lin & Wei Jin & Fushuan Wen, 2023. "A Brief Survey on the Development of Intelligent Dispatcher Training Simulators," Energies, MDPI, vol. 16(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:706-:d:1028147
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