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Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System

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5 Author(s)
Nagi, J. ; Dept. of Electron. & Commun. Eng., Univ. Tenaga Nasional, Kajang, Malaysia ; Keem Siah Yap ; Sieh Kiong Tiong ; Ahmed, S.K.
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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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Power Delivery, IEEE Transactions on  (Volume:26 ,  Issue: 2 )