Error Modeling and Anomaly Detection of Smart Electricity Meter Using TSVD+L Method

Abstract

Using remote data analysis to estimate smart electricity meters (SMs) and detect SMs&#x2019; anomaly, has aroused considerable discussion in power industry, because its lower cost and higher efficiency compared to traditional field calibration. However, the trouble of lacking topology and parameter of the distribution grid and the ill-posed problem in the SMs&#x2019; error estimation and anomaly detection (AD) are not well resolved in most energy conservation theorem-based SMs AD methods. This paper presents a sorted Top-N anomaly detection mechanism to generate a list of suspicious anomalous SMs. The error estimation model (EEM) only using SMs electricity consumption data is investigated. The truncated singular value decomposition regularization with L-curve optimization (TSVD+L) method is proposed to address the model&#x2019;s ill-posedness. Three data processing modes, namely one-pot mode, segmentation mode and sliding window technique (SWT), are suggested to obtain multiple calculation results for SMs error comprehensive evaluation. The top <i>N</i>% SMs in error sequence is proposed for onsite calibration instead of full inspection. The effectiveness and practicality of the proposed method are verified through both simulation case and practical distribution network application. The results show that the proposed method has higher accuracy in SMs anomaly detection, compared with the ordinary least squares (OLS) method, recursive least squares (RLS) method, and Tikhonov regularization (Tik) method.