基于状态特征聚类的非侵入式负荷事件检测方法

doi:10.19595/j.cnki.1000-6753.tces.190627

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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.

Key words： Non-intrusive load monitoring event detection state clustering Mean-shift algorithm

Received: 23 May 2019

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TM714

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	Articles by authors
	Zhou Dongguo
	Zhang Heng
	Zhou Hong
	Hu Wenshan

Cite this article:

Zhou Dongguo,Zhang Heng,Zhou Hong等. Non-Intrusive Load Event Detection Method Based on State Feature Clustering[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4565-4575.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190627 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I21/4565

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