A Physically Inspired Data-Driven Model for Electricity Theft Detection With Smart Meter Data

Electricity theft is the third largest form of theft in the United States. It not only leads to significant revenue losses, but also creates the risk of fires and fatal electrical shocks. In the past, utilities have fought electricity theft by sending field operation groups to conduct physical inspections of electrical equipment based on suspicious activity reported by the public. However, the recent rapid penetration of advanced metering infrastructure makes it possible to detect electricity theft by analyzing the information gathered from smart meters. In this paper, we develop a physically inspired data driven model to detect electricity theft with smart meter data. The main advantage of the proposed model is that it only leverages the electricity usage and voltage data from smart meters instead of unreliable parameter and topology information of the secondary network. Hence, a speedy and widespread adoption of the proposed model is feasible. We show that a modified linear regression model accurately captures the physical relationship between electricity usage and voltage magnitude on the Kron-reduced distribution secondaries. Our results show that electricity theft on a distribution secondary will lead to negative and positive residuals from the regression for dishonest and honest customers, respectively. The proposed model is validated with real-world smart-meter data. The results show that the model is effective in identifying electricity theft cases.