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Article type: Research Article
Authors: Qiao, Junfenga; * | Peng, Lina | Zhou, Aihuaa | Pan, Sena | Yang, Peia | Xu, Mina | Shen, Xiaofengb | Chen, Jingdeb | Gu, Huab
Affiliations: [a] State Grid Smart Grid Research Institute Co., LTD, State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology, Nanjing, Jiangsu, China | [b] Qingpu Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai, China
Correspondence: [*] Corresponding author. Junfeng Qiao, State Grid Smart Grid Research Institute Co., LTD, State Grid Laboratory of Power cyber-Security Protection & Monitoring Technology, Nanjing, Jiangsu, 210013, China. E-mail: universitymail@126.com.
Abstract: This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction.
Keywords: Recurrent neural network, power equipment fault prediction, index trend curve, fault feature sample set, power supply reliability
DOI: 10.3233/JIFS-236459
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8025-8035, 2024
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