高压电器2017，Vol.53Issue(12)：195-201,207,8.DOI:10.13296/j.1001-1609.hva.2017.12.032
装备电力系统电能质量复合扰动识别方法研究
Complex Disturbances Classification of Equipment Power Quality
摘要
Abstract
In improve the recognition ability of equipment power quality complex disturbances,a new approach is proposed to deal with identification and classification of power quality complex disturbances based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) with combined feature extraction.The combination of S-transform and Empirical Mode Decomposition (EMD) is employed to construct a set of composite feature vectors in order to represent the complex disturbances comprehensively.ELM training error is used as the fitness function of PSO to optimize the hidden layer neuron number.The initial parameters of PSO are set up to complete the classifier design,and the higher classification accuracy is maintained on the basis of improving the classification speed.Test results show that the new approach can accurately recognize seven types of power quality complex disturbances of the equipment power system,and has strong noise immunity.Compared with the non-optimized ELM,the new approach reduces the training and classification time,and improves the recognition accuracy.关键词
装备电力系统/复合扰动/分类识别/粒子群算法/极限学习机/组合特征提取Key words
equipment power system/complex disturbances/classification/particle swarm optimization (PSO)/extreme learning machine(ELM)/combined feature extraction引用本文复制引用
尹志勇,陈永光,刘金宁,桑博..装备电力系统电能质量复合扰动识别方法研究[J].高压电器,2017,53(12):195-201,207,8.基金项目
国家自然科学基金项目资助(51307184).Project Supported by National Natural Science Foundation of China(51307184). （51307184）
