Non-Technical Loss Detection in Power Grids with Statistical Profile Images Based on Semi-Supervised Learning

• Main Authors: Jiangteng Li, Fei Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-12-01
• Series: Sensors
• Subjects: sensor system; smart meter; non-technical loss; deep learning; semi-supervised learning
• Online Access: https://www.mdpi.com/1424-8220/20/1/236

In order to keep track of the operational state of power grids, the world’s largest sensor system, smart grid, was built by deploying hundreds of millions of smart meters. Such a system makes it possible to discover and make quick response to any hidden threat to the entire power grid. Non-technical losses (NTLs) have always been a major concern for their consequent security risks as well as immeasurable revenue loss. However, various causes of NTL may have different characteristics reflected in the data. Accurately capturing these anomalies faced with such a large scale of collected data records is rather tricky as a result. In this paper, we proposed a new methodology of detecting abnormal electricity consumptions. We did a transformation of the collected time-series data which turns it into an image representation that could well reflect users’ relatively long term consumption behaviors. Inspired by the excellent neural network architecture used for objective detection in computer vision, we designed our deep learning model that takes the transformed images as input and yields joint features inferred from the multiple aspects the input provides. Considering the limited amount of labeled samples, especially the abnormal ones, we used our model in a semi-supervised fashion that was brought about in recent years. The model is tested on samples which are verified by on-field inspections and our method showed significant improvement for NTL detection compared with the state-of-the-art methods.