Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
20/05/2014
20/05/2014
01/10/2011
|
Resumo |
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) This paper proposes a computational technique for the classification of electricity consumption profiles. The methodology is comprised of two steps. In the first one, a C-means-based fuzzy clustering is performed in order to find consumers with similar consumption profiles. Afterwards, a fuzzy classification is performed using a fuzzy membership matrix and the Euclidean distance to the cluster centers. Then, the distance measures are normalized and ordered, yielding a unitary index score, where the potential fraudsters or users with irregular patterns of consumption have the highest scores. The approach was tested and validated on a real database, showing good performance in tasks of fraud and measurement defect detection. |
Formato |
2436-2442 |
Identificador |
http://dx.doi.org/10.1109/TPWRD.2011.2161621 IEEE Transactions on Power Delivery. Piscataway: IEEE-Inst Electrical Electronics Engineers Inc, v. 26, n. 4, p. 2436-2442, 2011. 0885-8977 http://hdl.handle.net/11449/8928 10.1109/TPWRD.2011.2161621 WOS:000298981800041 |
Idioma(s) |
eng |
Publicador |
Institute of Electrical and Electronics Engineers (IEEE) |
Relação |
IEEE Transactions on Power Delivery |
Direitos |
closedAccess |
Palavras-Chave | #Data mining #electricity theft #fuzzy clustering #nontechnical losses |
Tipo |
info:eu-repo/semantics/article |