Non-technical losses detection employing adversarial domain adaptation

Abstract

Non-technical losses (NTL) in power grid not only cause significant financial loss but also reduce the quality of supply for the distribution network operators (DNOs). In recent years, the frequency of power theft activities related to NTL have been increased rapidly. The data-based detection approach for NTLs has been studied by researchers from all over the world thoroughly, but the region domain shift issues and the lack of labelled data limited its applicability. In this paper, an adversarial learning-based approach is proposed to alleviate the domain shift problem, named Cross-Regional Adaptive Network (CAN). First of all, a multilayer Convolutional Neural Networks (CNN) feature extractor maps the training and application scenario data into the same feature space. After that the feature extractor and the domain classifier are trained against each other by inversing extractor’s gradients. Finally, the training and application scenario data are indistinguishable for the domain classifier, and the feature extractor can extract domain invariant features. Experiments are conducted on two sets of electricity consumption data captured by utility company to verify the effectiveness of the proposed method.