A Bayesian-based classification method of impulse waveforms for non-intrusive load monitoring application
ZHANG Bo1, LIANG Kai2
1. Xuzhou Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd, Xuzhou, Jiangsu 221006; 2. Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd, Wuxi, Jiangsu 214002
Abstract：Aiming at the problem of impact load type identification in non-intrusive load monitoring, an impact waveform classification method based on Bayesian classifier is proposed. Firstly, the characteristic parameters of an impulse current waveform such as impulse amplitude, rising time, dropping amplitude, and falling time are defined to establish a multi-feature Bayesian classification model. Secondly, the current impulse waveform samples of different appliances are divided into several groups, and the mean values of the characteristic values of the samples are used as the parameters of the classification model. Finally, the classification algorithm is implemented on the hardware platform of single-phase watt hour meter. The classification tests of fixed frequency air conditioner and variable frequency air conditioner are carried out in the laboratory scene. The results show that the proposed method can effectively identify two types of impulse waveforms, which verifies the feasibility of the proposed method.
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2022, Vol. 23 
