Nontechnical Losses Detection Through Coordinated BiWGAN and SVDD

• Published in: IEEE transaction on neural networks and learning systems 2021-05, Vol.32 (5), p.1866-1880
• Main Authors: Hu, Tianyu, Guo, Qinglai, Sun, Hongbin, Huang, Tian-En, Lan, Jian
• Format: Article
• Language: English
• Subjects: Algorithms; Anomalies; Anomaly detection; bidirectional Wasserstein GAN and support vector data \hbox{description-based} NTL detector (BSBND); Consumption; Deep learning; Feature extraction; Gallium nitride; generative adversarial network (GAN); Generative adversarial networks; Kernel; Machine learning; nontechnical losses’ (NTLs) detection; smart meters; support vector data description (SVDD); Support vector machines; Task analysis
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2020.2994116

Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges: the inefficiency of harnessing high dimensionality and the severe lack of fraudulent samples. To overcome them, an NTL detection model based on deep learning and anomaly detection is proposed in this article, namely bidirectional Wasserstein GAN and support vector data description-based NTL detector (BSBND). Motivated by the powerful ability of generative adversarial networks (GANs) to learn deep representation from high-dimensional distributions of data, in the BSBND, we utilized a BiWGAN for feature extraction from high-dimensional raw consumption records, and a one-class classifier trained only on benign samples-SVDD-is adopted to map features into judgments. Moreover, a novel alternate coordinating algorithm is proposed to optimize the cooperation between the upstream BiWGAN and the downstream SVDD, and also, an interpreting algorithm is proposed to visualize the basis of each fraudulent judgment. Case studies have demonstrated the superiority of the BSBND over the state of the arts, the powerful feature extraction ability of BiWGAN, and also the effectiveness of the proposed coordinating and interpreting algorithms.