摘要
为提高输电线路雷电过电压识别结果的准确性,采用非线性因子递减和引入动态权重2种策略对灰狼优化算法进行改进,以提高IGWO算法的优化性能。以雷电过电压特征参数为输入量,采用IGWO算法对SVM进行参数优化,建立基于IGWO-SVM的输电线路雷电过电压识别模型。算例分析结果表明,所提IGWO-SVM模型过电压识别结果的正确率高达96%,识别精度高于其他模型,验证了IGWO-SVM模型的正确性和优越性。
In order to improve the accuracy of lightning overvoltage identification results of transmission lines,the grey wolf optimization algorithm is improved by using two strategies nonlinear factor decreasing and introducing dynamic weight,so as to improve the optimization performance of IGWO algorithm.Taking the characteristic parameters of lightning overvoltage as the input,the IGWO algorithm is used to optimize the parameters of SVM,and a lightning overvoltage identification model for transmission lines based on IGWO-SVM is established.The analysis results of an example show that the correct rate of the overvoltage identification results of the IGWO-SVM model proposed is 96%,and the identification accuracy is higher than other models,which verifies the correctness and superiority of the IGWO-SVM model.
作者
雷稀童
吴宇峰
陈海旭
刘闯
胡少华
LEI Xitong;WU Yufeng;CHEN Haixu;LIU Chuang;HU Shaohua(Fuzhou Yili Power Engineering Co.,Ltd.,Fuzhou 350000,China;Fuzhou Power Supply Company,State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350000,China;Jingmen Power Supply Company,State Grid Hubei Electric Power Co.,Ltd.,Jingmen 448000,China)
出处
《黑龙江电力》
CAS
2023年第6期481-486,共6页
Heilongjiang Electric Power
基金
国家电网公司科技项目(多种储能和储电系统的优化配置及控制研究)(项目编号:SGTYHT/17-JS-199)。
关键词
输电线路
雷电过电压
识别模型
支持向量机
transmission line
lightning overvoltage
identification model
support vector machine
