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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Nagi, J. Yap, K.S. Tiong, S.K. Ahmed, S.K. Mohammad, A.M. |
| Copyright Year | 2008 |
| Abstract | Efficient methods for detecting electricity fraud has been an active research area in recent years. This paper presents a hybrid approach towards non-technical loss (NTL) analysis for electric utilities using genetic algorithm (GA) and support vector machine (SVM). The main motivation of this study is to assist Tenaga Nasional Berhad (TNB) in Malaysia to reduce its NTLs in the distribution sector. This hybrid GA-SVM model preselects suspected customers to be inspected onsite for fraud based on abnormal consumption behavior. The proposed approach uses customer load profile information to expose abnormal behavior that is known to be highly correlated with NTL activities. GA provides an increased convergence and globally optimized SVM hyper-parameters using a combination of random and prepopulated genomes. The result of the fraud detection model yields classified classes that are used to shortlist potential fraud suspects for onsite inspection. Simulation results prove the proposed method is more effective compared to the current actions taken by TNB in order to reduce NTL activities. |
| Sponsorship | IEEE Region 10 |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 396942 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424424085 |
| DOI | 10.1109/TENCON.2008.4766403 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-11-19 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Genetic algorithms Inspection Power industry Support vector machine classification Data mining Biological cells Algorithm design and analysis Convergence Genomics load profile Support vector machine genetic algorithm electricity theft non-technical loss |
| Content Type | Text |
| Resource Type | Article |
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