东北电力技术2025，Vol.46Issue(5)：18-22,57,6.
基于改进鲸鱼算法优化支持向量机的电压暂降源识别
Voltage Sag Source Identification Based on Support Vector Machine Optimized by IWOA
胡俊 1李小龙 1张喻 1万亮1
作者信息
- 1. 国网湖北省电力有限公司钟祥市供电公司,湖北 荆门 431900
- 折叠
摘要
Abstract
In order to improve the accuracy of voltage sag source identification,a voltage sag source identification method based on the improved whale optimization algorithm(IWOA)optimized support vector machine(SVM)is proposed.Firstly,by using S variable losses to process the voltage signal,16 feature quantities are obtained for voltage sag source identification.Secondly,multiple mea-sures such as population chaos initialization,nonlinear convergence factor,and Cauchy mutation are adopted to improve the whale op-timization algorithm,resulting in the IWOA algorithm with better convergence performance.The IWOA algorithm is used to search for the optimal parameter values of SVM,and a voltage sag source identification model based on IWOA-SVM is constructed.Six different categories of voltage sag disturbance signals are used for example analysis.The results show that the recognition accuracy of the IWOA-SVM model is as high as 98.33%,and the recognition effect is better than other methods,verifying the effectiveness of the proposed voltage sag source identification method.关键词
电压暂降/识别/改进鲸鱼算法/支持向量机/S变换Key words
voltage sag/identification/IWOA/support vector machine/S variable losses分类
信息技术与安全科学引用本文复制引用
胡俊,李小龙,张喻,万亮..基于改进鲸鱼算法优化支持向量机的电压暂降源识别[J].东北电力技术,2025,46(5):18-22,57,6.
