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This study proposes a fuzzy self-organized neural networks (SOM) model for detecting fraud by domestic customers, the major cause of non-technical losses in power distribution networks. Using a bottom-up approach, normal behavior patterns of household loads with and without photovoltaic (PV) sources are determined as normal behavior. Customers suspected of energy theft are distinguished by calculating the anomaly index of each subscriber. The bottom-up method used is validated using measurement data of a real network. The performance of the algorithm in detecting fraud in old electromagnetic meters is evaluated and verified. Types of energy theft methods are introduced in smart meters. The proposed algorithm is tested and evaluated to detect fraud in smart meters also.
Alireza Vahabzadeh; Alibakhsh Kasaeian; Hasan Monsef; Alireza Aslani. A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV. Energies 2020, 13, 1287 .
AMA StyleAlireza Vahabzadeh, Alibakhsh Kasaeian, Hasan Monsef, Alireza Aslani. A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV. Energies. 2020; 13 (5):1287.
Chicago/Turabian StyleAlireza Vahabzadeh; Alibakhsh Kasaeian; Hasan Monsef; Alireza Aslani. 2020. "A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV." Energies 13, no. 5: 1287.