电力系统保护与控制2024，Vol.52Issue(13)：115-124,10.DOI:10.19783/j.cnki.pspc.231283
基于ISSA-XGBoost的电能质量扰动识别方法研究
A power quality disturbance identification method based on ISSA-XGBoost
摘要
Abstract
To solve the problem of redundancy and low recognition accuracy in traditional power quality disturbance recognition,this paper proposes a method based on an improved sparrow search algorithm(ISSA)to optimize feature selection and eXtreme gradient boosting(XGBoost).First,the power quality disturbance signal is transformed by S-transform,and 61 kinds of power quality characteristics are extracted.Then ISSA selects the optimal feature subset and the optimal parameters in XGBoost at the same time to eliminate redundant features and improve recognition accuracy.Finally,the power quality disturbance is identified according to the optimized feature subset and XGBoost.The simulation results show that the method proposed can effectively select the optimal feature subset,and efficiently identify 19 kinds of power quality disturbance signals in a noisy environment,and has high recognition accuracy.关键词
电能质量/扰动识别/XGBoost/麻雀搜索算法Key words
power quality/disturbance identification/XGBoost/sparrow search algorithm引用本文复制引用
商立群,李朝彪,邓力文,郝天奇,刘晗..基于ISSA-XGBoost的电能质量扰动识别方法研究[J].电力系统保护与控制,2024,52(13):115-124,10.基金项目
This work is supported by the Natural Science Basic Research Plan of Shaanxi Province(No.2021JM393). 陕西省自然科学基础研究计划项目资助(2021JM393) （No.2021JM393）
