Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity

被引：0
|
作者：
Duan J. [1 ]
Li Y. [1 ]
Zhang Z. [1 ]
Li W. [2 ]
Jiang L. [3 ]
Li L. [4 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
[2] Changsha Xinao Changran Energy Development Co., Ltd., Changsha
[3] Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool
[4] Changsha Xinao Xiangjiang New Energy Development Co., Ltd., Changsha
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 24期
基金：
中国国家自然科学基金;
关键词：
Hidden Markov model; Mutual information; Non-intrusive load identification; Residual neural network;
D O I：
10.7500/AEPS20210416001
中图分类号：
学科分类号：
摘要：
Non-intrusive load monitoring technology can guide users to arrange power consumption time reasonably, thereby reducing power consumption. Among them, due to the continuous variability of the state, the identification of continuously varying (Type-Ⅲ) load has always been one of the difficult problems in non-intrusive load monitoring. Aiming at the problem of Type-Ⅲ load identification, a non-intrusive load identification algorithm based on deep convolutional neural network (CNN) and hidden Markov model (HMM) is proposed. Firstly, the load characteristics are selected according to the mutual information theory. Then, the residual neural network is used as the basic structure of deep CNN to extract multi-dimensional features of the load and realize the initial identification of Type-Ⅲ loads. Finally, in order to solve the problem of state breakpoint in CNN identification results, the HMM is used to complete the continuous optimization of load identification results. In the complex industrial and commercial operation environment, the algorithm is trained and verified on the representative Type-Ⅲ load data. The results show that the proposed algorithm can effectively identify the operation state of Type-Ⅲ industrial and commercial load. © 2021 Automation of Electric Power Systems Press.
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收藏
页码：65 / 72
页数：7
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