A Brief Survey on the Development of Intelligent Dispatcher Training Simulators

Ao Dong, Xinyi Lai, Chunlong Lin, Changnian Lin, Wei Jin and Fushuan Wen ()
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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
Changnian Lin: Beijing Kedong Electric Control System Co., Ltd., Haidian District, Beijing 100192, China
Wei Jin: Beijing Kedong Electric Control System Co., Ltd., Haidian District, Beijing 100192, China
Fushuan Wen: School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2023, vol. 16, issue 2, 1-17

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.

Keywords: dispatcher training simulator (DTS); dynamic simulation; intelligent training; artificial intelligence; adaptive learning; Felder-Silverman index; Adaptive Educational Hypermedia Systems (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: 2023
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