Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN

doi:10.11930/j.issn.1004-9649.202103090

Abstract

Abstract: In view of the complex relationship between UHV transmission line loss and its characteristic parameters, this paper proposes a radial basis function neural network (RBFNN) model improved by use of the Canopy-K-means clustering algorithm and the adaptive second mutation differential evolution (ASMDE) algorithm to predict the UHV transmission line loss. The characteristic parameters of UHV transmission line loss determined by theoretical analysis are clustered through Canopy-K-means clustering algorithm to determine the hidden layer nodes of radial basis function (RBF) neural network, subsequently ensuring the RBF neural network to have a better hidden layer center. The RBF neural network optimized by ASMDE algorithm is trained with the sample data of characteristic parameters and line loss, so as to fit the complex nonlinear relationship between line loss and characteristic parameters. Finally, the historical data of a UHV transmission line in Central China is taken for simulation, and the results have verified the practicability and effectiveness of the proposed method.

Key words: UHV, line loss, RBFNN, Canopy-K-means algorithm

YANG Jianhua, XIAO Daqiang, ZHANG Wei, YU Mingqiong, YI Benshun. Prediction of 1 000 kV UHV Line Loss Based on Improved RBFNN[J]. Electric Power, 2022, 55(5): 122-127,142.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202103090

https://www.electricpower.com.cn/EN/Y2022/V55/I5/122

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