Application of artificial intelligence in electric power dispatching automation system
DOI: 10.23977/jeeem.2024.070310 | Downloads: 13 | Views: 196
Author(s)
Renjie Mao 1, Wenzhe Liu 2, Jing Qiu 1
Affiliation(s)
1 The University of Sydney, Camperdown NSW, 2050, Australia
2 National University of Defense Technology (NUDT), Changsha, China
Corresponding Author
Renjie MaoABSTRACT
With the continuous expansion of the scale of the power system, the traditional power dispatching automation system is facing many challenges. In view of the problems of low data processing efficiency and poor load prediction accuracy and low fault diagnosis accuracy in the power dispatching automation system, the application of artificial intelligence technology in the power dispatching automation system is deeply analyzed. By employing a deep learning algorithm to analyze massive amounts of operational data, a load prediction model based on neural networks was constructed. Additionally, fuzzy reasoning and genetic algorithms were utilized to achieve intelligent fault diagnosis. The results indicate that the deep learning-based load prediction model is 15% more accurate than traditional methods. Furthermore, the fuzzy reasoning system can effectively identify over 90% of fault types, and the genetic algorithm can reduce fault location time by 40%. The application of artificial intelligence technology has significantly enhanced the intelligence level of power dispatching automation systems, providing robust support for the stable operation of the power system.
KEYWORDS
Power Dispatching; Automation System; Deep Learning; Fuzzy ReasoningCITE THIS PAPER
Renjie Mao, Wenzhe Liu, Jing Qiu, Application of artificial intelligence in electric power dispatching automation system. Journal of Electrotechnology, Electrical Engineering and Management (2024) Vol. 7: 75-81. DOI: http://dx.doi.org/10.23977/jeeem.2024.070310.
REFERENCES
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[2] Su Peipei, Geng Xiaona, Shan Desai. Application of artificial intelligence in electric power dispatching automation system [J]. Integrated circuit applications, 2023, 40 (11): 250-251.
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