山东电力技术2025，Vol.52Issue(3)：59-67,9.DOI:10.20097/j.cnki.issn1007-9904.2025.03.007
基于时频域多特征和优化KELM的电能质量扰动检测
Power Quality Disturbance Detection Based on Time-frequency Domain Multi-features and Optimized KELM
摘要
Abstract
Accurate classification of power quality disturbances is the premise for improving and controlling power quality.In order to improve the accuracy of rapid detection of power quality,this paper proposes a power quality disturbance(PQD)classification method based on time-frequency multi-features and improved kernel extreme learning machine(KELM).This method first uses wavelet transform and S transform to extract the feature quantities of each power quality disturbance signal.Then a KELM model with classification rules is constructed based on the extracted feature quantities,and chaotic particle swarm optimization(CPSO)is used to adaptively optimize the parameters of KELM.Example simulation results and comparative analysis show that this method can effectively identify seven kinds of common single disturbance signals and ten kinds of compound disturbance signals.The proposed method has stronger anti-noise ability,and its classification accuracy is higher than the KELM and PSO-KELM models,providing new ideas for the improvement and management of power quality.关键词
电能质量扰动分类/时频多特征/混沌粒子群优化/核极限学习机Key words
classification of power quality disturbances/time-frequency multi-features/chaotic particle swarm optimization/kernel extreme learning machine分类
动力与电气工程引用本文复制引用
徐琳,范松海,赵淳,隗震,刘畅..基于时频域多特征和优化KELM的电能质量扰动检测[J].山东电力技术,2025,52(3):59-67,9.基金项目
国家电网有限公司科技项目"农网台区末端融合感知、智能诊断及服务提升技术研究及应用"(52199922000M).Science and Technology Project of State Grid Corporation of China"Research and Application of Fusion Perception,Intelligent Diagnosis and Service Improvement Technology at the Terminal of Rural Grid Stations"(52199922000M). （52199922000M）
