Parallel hidden Markov model based classification of power quality disturbance events
DOI：10.7667/PSPC180062
Key Words:power quality  maximum overlapping discrete wavelet transform  parallel hidden Markov model  classification and identification
Author NameAffiliation
XIE Shanyi Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China 
XIAO Fei Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 
AI Qian Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 
ZHOU Gang Electric Power Research Institute of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China 
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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. This work is supported by Science and Technology Project of Guangdong Power Grid Company (No. GDKJXM20162540) and National High-tech R & D Program of China (863 Program) (No. 2015AA050404).
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