Detection of energy theft and defective smart meters in smart grids using linear regression. (October 2017)

• Record Type: Journal Article
• Authors: Yip, Sook-Chin; Wong, KokSheik; Hew, Wooi-Ping; Gan, Ming-Tao; Phan, Raphael C.-W.; Tan, Su-Wei
• Is Part Of: International journal of electrical power & energy systems. Volume 91(2017)
• Page Start: 230
• Page End: 240
• Publication Date: 2017-10
• Subjects: Energy theft detection -- Defective meter detection -- Smart Grid -- Linear regression -- Categorical variable; NTL non-technical loss -- UP utility provider -- SG Smart Grid -- SM smart meter -- AMI Advanced Metering Infrastructure -- NAN neighborhood area network -- MLR multiple linear regression -- RFID radio frequency identification -- SVM support vector machine -- GA genetic algorithm -- LUD LU decomposition -- DS distribution station -- TL technical loss -- LSE linear system of equations -- TOU Time-of-Use; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2017.04.005
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Two linear regression schemes to detect energy theft and faulty meter are proposed. Any non-zero anomaly coefficients may indicate energy theft or metering defect. The first scheme becomes unstable when energy thieves cheat inconsistently. Detection coefficients are introduced to handle inconsistent cheating behaviors. Energy thieves can be detected regardless of cheating behaviors. Abstract: The utility providers are estimated to lose billions of dollars annually due to energy theft. Although the implementation of smart grids offers technical and social advantages, the smart meters deployed in smart grids are susceptible to more attacks and network intrusions by energy thieves as compared to conventional mechanical meters. To mitigate non-technical losses due to electricity thefts and inaccurate smart meters readings, utility providers are leveraging on the energy consumption data collected from the advanced metering infrastructure implemented in smart grids to identify possible defective smart meters and abnormal consumers' consumption patterns. In this paper, we design two linear regression-based algorithms to study consumers' energy utilization behavior and evaluate their anomaly coefficients so as to combat energy theft caused by meter tampering and detect defective smart meters. Categorical variables and detection coefficients are also introduced in the model to identify the periods and locations of energy frauds as well as faulty smart meters. Simulations are conducted and the results show that the proposed algorithms can successfully detect all the fraudulent consumers and discover faulty smart meters in a neighborhood area network.