一种基于多参量隐马尔可夫模型的负荷辨识方法

张丽; 张涛; 张宏伟; 王福忠; 郭江震

引用本文:	张丽,张涛,张宏伟,等.一种基于多参量隐马尔可夫模型的负荷辨识方法[J].电力系统保护与控制,2019,47(20):81-90.
	ZHANG Li,ZHANG Tao,ZHANG Hongwei,et al.Research on a method of load identification based on multi parameter hidden Markov model[J].Power System Protection and Control,2019,47(20):81-90

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一种基于多参量隐马尔可夫模型的负荷辨识方法
张丽¹,张涛¹,张宏伟²,王福忠¹,郭江震¹
(1.河南理工大学电气工程与自动化学院,河南焦作 454000; 2.国网山西省电力公司翼城县供电公司,山西翼城 043500)

摘要:

由于电力需求侧负荷形态各异、特性多变，种类繁多，采用传统方法进行负荷辨识时存在识别率不高、模型建立困难、难以推广应用等问题。为此，基于智能负荷控制器(SRLC)的用电参数检测功能和非侵入式负荷监测(NILM)原理，提出一种基于多参量隐马尔可夫模型(MPHMM)的负荷辨识方法。该方法采用4个负载特性参数(电流、有功功率、无功功率、功率因素)作为模型的观测向量，通过模型学习和多次迭代计算，求得与MPHMM模型隐藏状态相匹配的观测序列的最大输出概率和最优状态序列，再采用辅助判别算法对结果进行修正，完成对负荷的最终辨识。通过搭建实验平台对所提方法进行验证。结果表明，该方法辨识准确率可达95%以上，特别是对小功率负荷具有较好的识别效果。

关键词: 负荷辨识非入侵式负荷监测多参量隐马尔科夫模型自动需求响应系统需求侧管理

DOI：10.19783/j.cnki.pspc.181360

分类号:

基金项目:国家自然科学基金项目资助(61403284);河南省开放实验室项目资助(KG2016-7);河南省高等学校重点科研项目资助(18A470014);河南理工大学博士基金项目资助(B2017-20)

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

ZHANG Li¹,ZHANG Tao¹,ZHANG Hongwei²,WANG Fuzhong¹,GUO Jiangzhen¹

(1. College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China;2. Yicheng Power Supply Company, State Grid Shanxi Electric Power Co., Ltd., Yicheng 043500, China)

Abstract:

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. This work is supported by National Natural Science Foundation of China (No. 61403284), Henan Open Laboratory Project (No. KG2016-7), Key Scientific Research Project of Colleges and Universities in Henan (No. 18A470014), and Doctoral Fund of Henan Polytechnic University (No. B2017-20).

Key words: load identification non-invasive load monitoring (NILM) multi parameter hidden Markov model (MPHMM) automatic demand response system (ADRS) demand side management (DSM)

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