红水河2024，Vol.43Issue(3)：100-106,7.DOI:10.3969/j.issn.1001-408X.2024.03.018
基于IGWO算法优化LSSVM的电能质量扰动识别方法
Power Quality Disturbance Identification Method Based on LSSVM Optimized by IGWO Algorithm
摘要
Abstract
In order to improve the accuracy of power quality disturbance(PQD)identification results,a PQD identification method based on improved grey wolf optimization algorithm(IGWO)optimized least squares support vector machine(LSSVM)is proposed.The grey wolf optimization algorithm is improved by using convergence factor index adjustment,adaptive displacement and weight dynamic revision,and the IGWO is obtained.Taking nine characteristic quantities of PQD signal as support vectors and seven PQD types as output quantities,the IGWO is used to find the optimal parameters of LSSVM,and the PQD recognition model based on IGWO-LSSVM is established.The simulation analysis is carried out and the recognition results are compared with those of other models.The results show that compared with several comparison models listed in the example,the IGWO-LSSVM model has higher recognition accuracy,which verifies the effectiveness and practicability of the proposed PQD recognition method.关键词
电能质量扰动/识别/改进灰狼优化算法/最小二乘支持向量机/S变换Key words
power quality disturbance/identification/improved grey wolf optimization/least squares support vector machine/s-transformation分类
信息技术与安全科学引用本文复制引用
江娜,彭震东,黄芳,尹凤梅,李巧玲..基于IGWO算法优化LSSVM的电能质量扰动识别方法[J].红水河,2024,43(3):100-106,7.基金项目
国家电网公司科技项目(5211HZ21004H) （5211HZ21004H）
