Research on a method of load identification based on multi parameter hidden Markov model

被引：0
|
作者：
Zhang L. [1 ]
Zhang T. [1 ]
Zhang H. [2 ]
Wang F. [1 ]
Guo J. [1 ]
机构：
[1] College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo
[2] Yicheng Power Supply Company, State Grid Shanxi Electric Power Co., Ltd., Yicheng
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 20期
基金：
中国国家自然科学基金;
关键词：
Automatic demand response system (ADRS); Demand side management (DSM); Load identification; Multi parameter hidden Markov model (MPHMM); Non-invasive load monitoring (NILM);
D O I：
10.19783/j.cnki.pspc.181360
中图分类号：
学科分类号：
摘要：
Due to the different forms, variable characteristics and various types of power loads on DSM, there are some problems in load identification using traditional methods, such as low recognition rate, difficulty in model building and difficulty in generalization. In this paper, a load identification method based on multi-parameter Hidden Markov Model is proposed, which is based on the intelligent load controller and NILM. Four load characteristic parameters are used as observation vectors of the model. Through model learning and iteration calculation, the maximum output probability and optimal state sequence of the observation sequence matching the hidden state of MPHMM model are obtained. Then the results are corrected by auxiliary discriminant algorithm to complete the final load identification. An experimental platform is built to verify the proposed method. The results show that the identification accuracy can reach more than 95% and it has good recognition effect for low power load especially. © 2019, Power System Protection and Control Press. All right reserved.
引用
收藏
页码：81 / 90
页数：9
相关论文
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