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.

中文翻译：

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利用智能电表数据进行窃电检测的物理启发数据驱动模型


窃电是美国第三大盗窃形式。它不仅会导致重大收入损失，还会造成火灾和致命电击的风险。过去，公用事业公司根据公众报告的可疑活动，派出现场作业小组对电气设备进行实物检查，以打击窃电行为。然而，最近先进计量基础设施的快速普及使得通过分析从智能电表收集的信息来检测窃电成为可能。在本文中，我们开发了一种物理启发的数据驱动模型，以利用智能电表数据检测窃电行为。该模型的主要优点是它仅利用智能电表的用电量和电压数据，而不是二次网络的不可靠参数和拓扑信息。因此，所提出的模型的快速和广泛采用是可行的。我们表明，修改后的线性回归模型可以准确地捕捉 Kron 缩减配电次级绕组上的用电量和电压幅度之间的物理关系。我们的结果表明，二次配电中的窃电将分别导致不诚实和诚实客户的回归产生负残差和正残差。所提出的模型通过现实世界的智能电表数据进行了验证。结果表明，该模型能够有效识别窃电案件。