电力系统保护与控制2019，Vol.47Issue(2)：80-86,7.DOI:10.7667/PSPC180062
基于并行隐马尔科夫模型的电能质量扰动事件分类
Parallel hidden Markov model based classification of power quality disturbance events
摘要
Abstract
In order to meet the requirements of accurately classifying power quality disturbances, a method for power quality disturbance classification is proposed based on Maximal Overlap Discrete Wavelet Transform (MODWT)and Parallel Hidden Markov Model (PHMM). Initially, a practical power quality disturbance detection algorithm is proposed by using MODWT. This algorithm can obtain the disturbance beginning and ending time accurately without setting detection threshold, from whose results the voltage harmonic components of power quality disturbance are extracted and used to form feature vector. Then, PHMM, as a classifier, is used to identify power quality disturbances. PHMM method solves the problem of poor convergence and longer training time for Artificial Neural Network (ANN)method, and thus the performance of the classifier is greatly improved. The test results based on power grid field data show that the proposed method is suitable for detecting various types of power quality disturbances, and it is characterized by high recognition correctness and less training time, and it will find extensive application.关键词
电能质量/极大重叠离散小波变换/并行隐马尔科夫模型/分类识别Key words
power quality/maximum overlapping discrete wavelet transform/parallel hidden Markov model/classification and identification引用本文复制引用
谢善益,肖斐,艾芊,周刚..基于并行隐马尔科夫模型的电能质量扰动事件分类[J].电力系统保护与控制,2019,47(2):80-86,7.基金项目
广东电网公司科技项目资助(GDKJXM20162540) （GDKJXM20162540）
国家863计划课题项目资助(2015AA050404). （2015AA050404）
