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- Title
基于CF-MF-SE联合特征的非侵入式负荷辨识.
- Authors
安国庆; 梁宇飞; 蒋子尧; 李 争; 安 琪; 陈 贺; 李 峥; 王 强; 白嘉诚
- 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.
- Subjects
PROBLEM solving; FEATURE selection; PARTICLE swarm optimization; SCIENTIFIC apparatus &; instruments; ENTROPY (Information theory)
- Publication
Journal of Hebei University of Science & Technology, 2021, Vol 42, Issue 5, p462
- ISSN
1008-1542
- Publication type
Article
- DOI
10.7535/hbkd.2021yx05004