A Fuzzy-SOM Method for Fraud Detection in Power Distribution Networks with High Penetration of Roof-Top Grid-Connected PV

Alireza Vahabzadeh, Alibakhsh Kasaeian, Hasan Monsef and Alireza Aslani
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Alireza Vahabzadeh: Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran
Alibakhsh Kasaeian: Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran
Hasan Monsef: School of Electrical and Computer Engineering, University of Tehran, Tehran 1417414418, Iran
Alireza Aslani: Faculty of New Sciences and Technologies, University of Tehran, Tehran 1439957131, Iran

Energies, 2020, vol. 13, issue 5, 1-24

Abstract: 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.

Keywords: fraud-detection; non-technical loss; power distribution; load profile modeling; data mining; fuzzy-SOM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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