Abstract：In view of the traditional noninvasive transient event detection methods are limited to a single electrical feature, they are prone to missed or false detection, and it is difficult to accurately perceive load events. This article uses the characteristics of state domain transition in the feature space when load events occur. A non-invasive load event detection method based on state feature clustering is proposed. In this method, the initial clustering points are searched and determined through the calculation of the difference value of sliding window, and then the Mean-shift algorithm is utilized for state clustering, and the load event occurrence points are determined according to the time domain distribution of the stable state, thus realizing load event detection. Finally, through the test of a variety of common electrical appliances in real experimental scenarios, and the comparison with some existing algorithms, the result shows that the proposed method can achieve load event detection more reliably, and lay the foundation for subsequent accurate load identification.
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2020, Vol. 35 
