Corona Loss Prediction of UHV AC Transmission Line Based on DBN Neural Network Optimized by PSO

doi:10.11930/j.issn.1004-9649.202112041

Abstract

Abstract: Based on the correlation between corona loss of UHV AC transmission lines and weather conditions such as rainfall, specific humidity, temperature, relative humidity, pressure, etc., a corona loss prediction method for UHV AC transmission lines is proposed by predicting corona loss of UHV AC transmission lines under some weather conditions. Based on the optimization mechanism of particle swarm optimization (PSO) and the prediction principle of deep belief network (DBN), the intelligent algorithm mechanism of this prediction method is explained in detail, and a complete set of prediction methods based on PSO—DBN intelligent algorithm is proposed. Firstly, the weather conditions that have strong correlation with corona loss are determined by the size of the Spearman correlation coefficient and used as the characteristic values. Then the DBN neural network is built with the selected eigenvalues as the index system to predict the corona loss. Then the internal parameters of the DBN neural network are adjusted by PSO optimization algorithm to improve the prediction accuracy of the DBN neural network. Finally, the proposed algorithm is used to predict the corona loss of the actual Fujian—Zhejiang UHV transmission line, and the statistical corona loss values of the line are compared and analyzed to verify the feasibility of the proposed prediction method. This method provides reference for corona loss research and engineering design of extra high voltage transmission lines.

Key words: UHV, corona loss, Spearman correlation coefficient, particle swarm optimization, deep belief network

HUANG Shumin, JIANG Lingao, LI Zhichuan, YANG Guangxu, SONG Fugen. Corona Loss Prediction of UHV AC Transmission Line Based on DBN Neural Network Optimized by PSO[J]. Electric Power, 2022, 55(6): 95-102,214.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202112041

https://www.electricpower.com.cn/EN/Y2022/V55/I6/95

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