Abstract
It is very important to extract discriminative electrical signal features in the lean management of the power grid. In this article, we develop a new current signal waveform feature. First, the electronic signal waveform is segmented into different segments with different properties, and then, the synthetic features, including shape feature, harmonic feature, and statistic features, are extracted. To illustrate the effectiveness of the synthetic features, we use them in noninvasive load monitoring (NILM). The similarity measurement is defined and calculated between each pair of segments based on the synthetic features, and the maximum clique searching algorithm is proposed to identify the consistent segments as state categories. The experimental results show that our method can significantly enhance precision and recall while identifying states in a more fine-grain style compared with state-of-the-art NILM methods.
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