基于CF-MF-SE联合特征的非侵入式负荷辨识

安国庆 梁宇飞 蒋子尧 李争 安琪 陈贺 李峥 王强 白嘉诚

摘 要：针对目前非侵入式负荷辨识存在模型训练时间过长以及负荷特征相近的电器辨识精度不高的问题，提出了一种基于CF-MF-SE联合特征的非侵入式负荷辨识方法。以稳态电流信号为基础，通过提取峰值因数表征波形的畸变程度，采用裕度因子表征信号的平稳程度，谱熵表征频谱结构复杂程度，并结合PSO-SVM实现负荷辨识。结果表明，新方法可解决电器电流波形相近不易识别的难题，减少训练时间，有效提高识别准确率和效率。所提方法将振动信号特征作为负荷特征引入负荷辨识领域，为非侵入式负荷辨识技术的特征选取提供了新思路，其中谱熵作为对负荷敏感的关键特征，与其他特征组合可明显提高辨识率，为实际应用中负荷特征的灵活选择提供了参考。

关键词：电气测量技术及其仪器仪表;非侵入式负荷辨识;谱熵;支持向量機;粒子群优化

中图分类号：TM933   文献标识码：A

doi：10.7535/hbkd.2021yx05004

收稿日期：2021-05-16;修回日期：2021-09-01;责任编辑：冯 民

基金项目：河北省省级科技计划资助（20311801D）;2020年通用航空增材制造协同创新中心课题（15号）

第一作者简介：安国庆（1995—），男，河北石家庄人，副教授，博士，主要从事电力设备状态监测方面的研究。

通讯作者：安 琪博士。E-mail：an-qi.122@163.com

Non-intrusive load identification based on CF-MF-SE joint feature

AN Guoqing1，2，LIANG Yufei1，JIANG Ziyao1，LI Zheng1，2，AN Qi1，

CHEN He2，LI Zheng2，WANG Qiang2，BAI Jiacheng1

（1.School of Electrical Engineering，Hebei University of Science and Technology，Shijiazhuang，Hebei 050018，China;2.Hebei Institute of Intelligent Distribution and Electric Equipment Technology （Shijiazhuang Kelin Electric Company Limited），Shijiazhuang，Hebei 050222，China）

Abstract：Aiming at the problems of the current non-intrusive load identification，such as too long model training time and low identification accuracy of electrical appliances with similar load characteristics，a non-intrusive load identification method based on CF-MF-SE joint feature was proposed.Based on the steady-state current signal，the peak factor was extracted to represent the distortion degree of the waveform，the margin factor was extracted to represent the stability degree of the signal，the spectral entropy was extracted to represent the complexity degree of the spectrum structure，and PSO-SVM was combined to realize load identification.Experimental results show that this method can solve the problem that the electrical current waveform is too similar to identify successfully，reduce the training time，and improve the recognition accuracy and efficiency.This method introduces the vibration signal characteristics as load characteristics into the field of load identification，which provides a new idea for feature selection of non-invasive load identification technology.As a key feature sensitive to load，spectral entropy can significantly improve the identification rate when combined with other features，which provides reference for the flexible selection of load characteristics in practical application.

Keywords：

electrical measuring technology and its instrumentation;non-intrusive load identification;spectral entropy;support vector machine;particle swarm optimization

近年来，电网负荷日益增加以及分布式清洁能源不断接入电网，对智能电网的发展提出了更高要求，“智能用电”和“绿色用电”成为当今研究热点[1]。负荷监测作为实现智能电网的第一步，通过检测某一环境内的总负荷获取内部各用电器的用电信息，获取的信息不仅可以帮助用户掌握电器的工作状态及能耗信息，而且可为电力公司进行电力部署提供依据。……