中圖分類號：TP183；TM73 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.223399 中文引用格式： 徐利美，閆磊，李遠(yuǎn)，等. 基于改進(jìn)EO-BP神經(jīng)網(wǎng)絡(luò)的高壓線損預(yù)測[J]. 電子技術(shù)應(yīng)用，2023，49(3)：82-88. 英文引用格式： Xu Limei，Yan Lei，Li Yuan，et al. High-voltage line loss prediction based on improved EO-BP neural network[J]. Application of Electronic Technique，2023，49(3)：82-88.
High-voltage line loss prediction based on improved EO-BP neural network
Xu Limei1，Yan Lei1，Li Yuan1，Yang She2，Ren Mifeng3
(1.State Grid Shanxi Electric Power Company，Taiyuan 030021， China； 2.Shanxi Extra High Voltage Substation Company of State Grid， Taiyuan 030021， China； 3.College of Electrical and Power Engineering， Taiyuan University of Technology，Taiyuan 030024， China)
Abstract： Aiming at the problem of low accuracy of high voltage line loss prediction, a line loss prediction model is proposed based on improved BP neural network and Equalization optimizer (EO) algorithm. Firstly, in order to improve the optimization ability of EO algorithm, a variety of chaotic mapping relations is used to initialize the population to increase the population diversity, then the global search ability could be improved. At the same time, the EO algorithm is improved by using the natural selection probability jump strategy, so that the model could jump out of the local optimization according to the probability and converge to the global optimal solution. Secondly, the improved EO algorithm is used to optimize the weight and bias of BP neural network, and the prediction effect of BP neural network for high voltage line loss is improved. Finally, the experimental results show that the proposed line loss prediction model has the highest prediction accuracy compared with regression model, BP neural network model, simulated annealing optimized BP neural network model and EO optimized BP neural network model.
Key words : line loss prediction；chaotic mapping；natural selection probability jump strategy；equilibrium optimizer algorithm；neural network
