Non-technical loss detection using state estimation and analysis of variance

Summary form only given. Illegal use of electric energy is a widespread practice in many parts of the world. Smart metering enables the improvement of customer load model, theft and stressed asset detections. In this paper, a state estimation based approach for distribution transformer load estimation is exploited to detect meter malfunction/tampering and provide quantitative evidences of non-technical loss (NTL). A measure of overall fit of the estimated values to the pseudo feeder bus injection measurements based on customer metering data aggregated at the distribution transformers is used to localize the electricity usage irregularity. Following the state estimation results, an analysis of variance (ANOVA) is used to create a suspect list of customers with metering problems and estimate the actual usage. Typical Taiwan Power Company (TPC) distribution feeder data are used in the tests. Results of NTL detection under meter defect and energy theft scenarios are presented. Experiences indicate that the proposed scheme can give a good trace of the actual usage at feeder buses and supplement the current meter data validation estimation and editing (VEE) process to improve meter data quality.