基于CF-MF-SE联合特征的非侵入式负荷辨识  

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

作　　者：安国庆 梁宇飞 蒋子尧 李争 安琪 陈贺 李峥 王强 白嘉诚 AN Guoqing;LIANG Yufei;JIANG Ziyao;LI Zheng;AN Qi;CHEN He;LI Zheng;WANG Qiang;BAI Jiacheng(School of Electrical Engineering,Hebei University of Science and Technology,Shijiazhuang,Hebei 050018,China;Hebei Institute of Intelligent Distribution and Electric Equipment Technology(Shijiazhuang Kelin Electric Company Limited),Shijiazhuang,Hebei 050222,China)

机构地区：[1]河北科技大学电气工程学院,河北石家庄050018 [2]河北省智能配用电装备产业技术研究院(石家庄科林电气股份有限公司),河北石家庄050222

出　　处：《河北科技大学学报》2021年第5期462-469,共8页Journal of Hebei University of Science and Technology

基　　金：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)。

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。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.

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 支持向量机 粒子群优化 

分 类 号：TM933[电气工程—电力电子与电力传动]