Abstract
Owing to the contagiousness of theft behaviors among customers, collaborative energy theft, such as village fraud, has become particularly common. In this study, a bunch of electricity thieves that steal energy at a constant ratio were considered. Conventional correlation-sorting-based methods may have some trouble handling these electricity thieves when they exist in the same area. To overcome such limitation, we firstly establish the mathematical model of non-technical loss (NTL) and the load data of fixed ratio electricity thieves (FRETs). Subsequently, an interesting correlation trend, which can be exploited to locate FRETs, was observed and analyzed. Based on this trend, we propose a correlation analysis-based detection method. It adopts a standardized covariance to measure the correlation between the NTL and user data. The detection of FRETs is realized by solving a combinatorial optimization problem. A corresponding framework in practice was also designed. Finally, numerical experiments based on a realistic dataset and an electricity theft dataset from an electricity theft emulator (ETE) are conducted to validate the effectiveness and superiority of the proposed method in terms of accuracy, stability, and scalability.
Highlights
Transmission loss in a power grid includes technical loss (TL) and non-technical loss (NTL) [1]
To effectively identify multiple fixed ratio electricity thieves (FRETs) in the same area, we proposed a detection method based on nontechnical loss covariance (NTLC)
This study proposes an NTLC-based method to detect FRETs and verifies the effectiveness of this method using a real consumption dataset provided by SGCC
Summary
Set of fraudulent users in the area. Csus,d Set of suspicious users on d-th day. P∗ Any user set having the increasing trend of correlation. Ui,t Recorded load for user i at time interval t. Ui Recorded load vector for user i. U ∗i Normalized recorded load vector for user i. U ∗i,d Normalized recorded load vector for user i on day d. Ωt NTL of an area at time interval t. Ω∗ Normalized NTL vector of an area. Ω∗d Normalized NTL vector of an area on d-th day. Coefficient vector composed of all the βi. Number of days of user i belongs to Csus,d. FUNCTIONS Corr(·, ·) Correlation measurement of two vectors. ρ(·, ·) Pearson correlation coefficient of two vectors. cov(·, ·) Covariance of two vectors. max(·) Maximum of a vector. mean(·) Arithmetic average of a vector
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