期刊
IEEE TRANSACTIONS ON POWER DELIVERY
卷 26, 期 2, 页码 1284-1285出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRD.2010.2055670
关键词
Computational intelligence system; fuzzy logic; nontechnical loss; pattern classification
资金
- Tenaga Nasional Berhad Distribution (TNBD) Sdn. Bhd.
- Tenaga Nasional Berhad Research (TNBR) Sdn. Bhd. [RJO 10061948]
This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy IF-THEN rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective.
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