电工技术学报2017，Vol.32Issue(3)：45-55,11.
一种实时电能质量扰动分类方法
A Method of Real-Time Power Quality Disturbance Classification
摘要
Abstract
In order to meet the requirements of classifying power-quality disturbances in real time,this paper proposes a new method based on the strong tracking filter (STF) and the extreme learning machine (ELM).STF is the modified version of the extended Kalman filter (EKF) by introducing the fading factor matrix to solve the problem of divergence.STF can not only detect the amplitude of the fundamental but also provide the fading factor as a feature identifying transient disturbances and harmonics.The proposed feature vector sets were composed of four features including the maximum and the minimum of the fundamental amplitude,the number of fluctuations,and the mean value of the fading factor frequentness.They were input into the ELM as the training examples to obtain a classifier for identifying disturbances.In addition,some rules were used to correct the error classification in a few boundary samples for attaining the higher accuracy.The simulation results show that the proposed method can identify 10 types of power quality disturbances including two complex disturbances,and have good noise immunity.And the higher accuracy can be achieved with less training and testing time compared with the stochastic gradient descent back-propagation (SGBP),least square support vector machine (LSSVM) and online sequential extreme learning machine method (OSELM).The proposed method is suitable for the online application.关键词
强跟踪滤波器/极限学习机/电能质量/渐消因子/扰动分类Key words
Strong tracking filter (STF)/extreme learning machine (ELM)/power quality/fading factor/disturbance classification分类
信息技术与安全科学引用本文复制引用
陈晓静,李开成,肖剑,孟庆旭,蔡得龙..一种实时电能质量扰动分类方法[J].电工技术学报,2017,32(3):45-55,11.基金项目
国家自然科学基金(51277080)、太阳能高效利用湖北省协同创新中心科研团队培育项目(HBSZD2014001)资助. （51277080）
