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

Jing DUAN; Yong LI; Zhenyu ZHANG; Weiwei LI; Lin JIANG; Lei LI

doi:10.7500/AEPS20210416001

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

Basic Research | 更新时间：2025-06-16

- Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity
- Automation of Electric Power Systems Vol. 45, Issue 24, Pages: 65-72(2021)
- 作者机构：
  
  1.湖南大学电气与信息工程学院，湖南省长沙市 410082
  2.长沙新奥长燃能源发展有限公司，湖南省长沙市 410005
  3.利物浦大学电气工程与电子系，利物浦L69 3BX，英国
  4.长沙新奥湘江新能源发展有限公司，湖南省长沙市 410005
- 作者简介：
- 基金信息：
  
  International Science and Technology Cooperation Program of China(2018YFE0125300);National Natural Science Foundation of China(52061130217);Huxiang Youth Talent Program of Hunan Province(2019RS1016)
- DOI：10.7500/AEPS20210416001
  CLC：
- Received：16 April 2021，
  
  Revised：2021-07-23，
  
  Published Online：17 September 2021，
  
  Published：25 December 2021
- 稿件说明：
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段晶,李勇,张振宇等.考虑辨识结果连续性的Type-Ⅲ型工商业负荷辨识方法[J].电力系统自动化,2021,45(24):65-72.

DUAN Jing,LI Yong,ZHANG Zhenyu,et al.Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity[J].Automation of Electric Power Systems,2021,45(24):65-72.
段晶,李勇,张振宇等.考虑辨识结果连续性的Type-Ⅲ型工商业负荷辨识方法[J].电力系统自动化,2021,45(24):65-72. DOI： 10.7500/AEPS20210416001.

DUAN Jing,LI Yong,ZHANG Zhenyu,et al.Identification Method for Type-Ⅲ Industrial and Commercial Load Considering Identification Result Continuity[J].Automation of Electric Power Systems,2021,45(24):65-72. DOI： 10.7500/AEPS20210416001.

摘要

非侵入式负荷监测技术可以引导用户合理安排用电时间，从而减少电量消耗。其中，由于状态的连续可变性，连续变化(Type-Ⅲ)型负荷的辨识一直是非侵入式负荷监测中难以解决的问题之一。针对Type-Ⅲ型负荷的辨识难题，提出了基于深度卷积神经网络(CNN)和隐马尔可夫模型(HMM)的非侵入式负荷辨识算法。首先，根据互信息理论进行负荷特征选择；然后，利用残差神经网络作为深度CNN的基本架构，提取负荷多维特征并实现Type-Ⅲ型负荷的初辨识；最后，为了解决CNN辨识结果中存在的状态断点问题，采用HMM完成负荷辨识结果的连续性优化。在复杂的工商业运行环境中，对具有代表性的Type-Ⅲ型负荷数据进行了算法训练和验证，结果表明所提算法能有效辨识Type-Ⅲ型工商业负荷的运行状态。

Abstract

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.

关键词

Keywords

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