摘要
针对高压线损预测精度不高的问题,提出一种基于均衡优化器(Equilibrium Optimizer,EO)和BP神经网络相结合的线损预测模型。首先,为了提高EO算法的寻优能力,利用多种混沌映射关系初始化种群,使种群多样性增加,全局搜索能力得到改善;同时,采用物竞天择概率跳脱策略改进EO算法,使模型依概率跳出局部最优而收敛于全局最优解。其次,采用改进的EO算法对BP神经网络的权值和偏置进行优化,进而改善BP神经网络的预测效果。最后,实验结果证明,所提线损预测模型相对于回归模型、BP神经网络模型、模拟退火算法优化BP神经网络模型和EO优化BP神经网络模型具有更高的预测精度。
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.
作者
徐利美
闫磊
李远
杨射
任密蜂
Xu Limei;Yan Lei;Li Yuan;Yang She;Ren Mifeng(State Grid Shanxi Electric Power Company,Taiyuan 030021,China;Shanxi Extra High Voltage Substation Company of State Grid,Taiyuan 030021,China;College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《电子技术应用》
2023年第3期82-88,共7页
Application of Electronic Technique
基金
山西省自然科学基金面上项目(20210302123189)。
关键词
线损预测
混沌映射
物竞天择概率跳脱策略
均衡优化器算法
神经网络
line loss prediction
chaotic mapping
natural selection probability jump strategy
equilibrium optimizer algorithm
neural network
