Improving SVM-Based Nontechnical Loss Detection in Power ... - Idsia

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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 2, APRIL 2011

Improving SVM-Based Nontechnical Loss Detection in Power Utility Using the Fuzzy Inference System Jawad Nagi, Keem Siah Yap, Sieh Kiong Tiong, Member, IEEE, Syed Khaleel Ahmed, Member, IEEE, and Farrukh Nagi Abstract—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. Index Terms—Computational intelligence system, fuzzy logic, nontechnical loss, pattern classification.

I. INTRODUCTION ISTRIBUTON losses in power utilities originating from electricity theft and other customer malfeasances are called nontechnical (NTLs). These losses mainly occur due to meter tampering, meter malfunction, illegal connections, billing irregularities, and unpaid bills [1]. The problem with NTLs is not only faced by the least developed countries in the Asian and African regions, but also by developed countries, such as the U.S. and the U.K. Specifically, high rates of NTL activities have been reported in the majority of developing countries in the Association of South East Asian Nations (ASEAN) group, which includes Malaysia, Indonesia, Thailand, and Vietnam [1]. As an example, in developing countries, such as Bangladesh, India, Pakistan, and Lebanon, an average between 20% to 30% of NTLs have been observed [2], [3]. In [2], the authors proposed a fraud detection model (FDM) to detect NTLs using support vector machines (SVMs) by utilizing load patterns of customers derived from the customer database. Based on the feedback from Tenaga Nasional Berhad Distribution (TNBD) Sdn. Bhd. for onsite customer inspection, the proposed

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Manuscript received October 06, 2009; revised March 22, 2010 and June 11, 2010; accepted June 21, 2010. Date of current version March 25, 2011. This work was supported in part by Tenaga Nasional Berhad Distribution (TNBD) Sdn. Bhd. and in part by Tenaga Nasional Berhad Research (TNBR) Sdn. Bhd. under Grant RJO 10061948. Paper no. PESL-00108-2009. J. Nagi and S. K. Tiong are with the Power Engineering Centre (PEC) and Department of Electronics and Communication Engineering of Universiti Tenaga Nasional, Kajang 43009, Selangor, Malaysia (e-mail: [email protected]; [email protected]). K. S. Yap and S. K. Ahmed are with the Department of Electronics and Communication Engineering, Universiti Tenaga Nasional, Kajang 43009, Selangor, Malaysia (e-mail: [email protected]; [email protected]). F. Nagi is with the Department of Mechanical Engineering, Universiti Tenaga Nasional, Kajang 43009, Selangor, Malaysia (e-mail: [email protected]). Digital Object Identifier 10.1109/TPWRD.2010.2055670

Fig. 1. Flowchart of the improved data postprocessing NTL framework for implementation and integration of the FIS into the FDM.

FDM in [2] achieved a detection hitrate of 60%. The approach proposed in this letter extends the research work of [2] by integrating human intelligence and knowledge into the SVM-based FDM with the introduction of a fuzzy inference system (FIS) [4] as a postprocessing scheme. The FIS acts as an intelligent decision-making system together with the SVM-based detection model in [2] to shortlist customer suspects with high probabilities of fraud activities and abnormalities. II. IMPLEMENTATION OF THE FIS The data postprocessing scheme in [2] employs a decisionmaking system using structured query language (SQL) to shortlist potential fraud customers from the correlated data. In context to [2, Fig. 6], Fig. 1 in this letter illustrates the flowchart of the improved data postprocessing NTL framework for the implementation and integration of the FIS into the current SVMbased FDM in [2]. A. Parameter Selection In order to select customers with higher probabilities of fraud from the correlated data in Fig. 1, useful parameters for the creation of a customer selection rule are determined by inspecting load profiles of customers confirmed as fraud by TNBD onsite inspection teams. From the correlated data, ten parameters are selected to construct a rule for the selection of customers with

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NAGI et al.: IMPROVING SVM-BASED NONTECHNICAL LOSS DETECTION IN POWER UTILITY

TABLE I PARAMETERS SELECTED FOR FORMATION OF THE CUSTOMER SELECTION RULE

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TABLE III COMPARISON OF THE DETECTION HITRATE FOR SVM AND SVM-FIS FRAMEWORK

E. Evaluation of the FIS

TABLE II FORMATION OF ORDINARY AND FUZZY RULE FOR CUSTOMER SELECTION

The FIS [4] is implemented by evaluating the fuzzy rule in Table II for all customers (FIS testing samples) shortlisted as fraud by the SVM in [2]. The FIS produces a fuzzified output value in between 0 to 1 for each testing sample (customer). Testing samples with a fuzzy output value of greater than 0.5 (default threshold in between 0 to 1) are considered as customers with higher probabilities of fraud activities and abnormalities. III. EXPERIMENTAL RESULTS Pilot testing results obtained from TNBD Sdn. Bhd. for onsite customer inspection by using the SVM-based FDM in [2] and the SVM-FIS FDM are indicated in Table III. The obtained results indicate that the computational intelligence scheme of SVM-FIS outperforms the SVM-based FDM in [2] by contributing a 12% increase in the average detection hitrate.

high probabilities of fraud and abnormalities, as indicated in Table I. B. Formation of the Customer Selection Rule The parameters selected in Table I are analyzed in order to develop an ordinary rule for the selection of customers with high probabilities of fraud activities and abnormalities. The customer selection rule is established by using discriminative features in order to classify (identify) consumption patterns of good customers [2, Fig. 4] against consumption patterns fraud customers [2, Fig. 3], as shown in Table II. The customer selection rule filters (removes) customers with low probabilities of fraud activities from the shortlisted suspects. C. Transformation of the Ordinary Rule Into Fuzzy Rule In order to implement fuzzy reasoning, the ordinary rule in Table II is transformed into a fuzzy IF-THEN rule, using definitions for combining fuzzy sets. For union operations (OR), the ” operation is used and for intersection operafuzzy set “ tions (AND), the “ ” operation is used. D. Fuzzy Membership Function Formulation and Tuning Triangular and trapezoid membership functions (MFs) using the ten parameter values defined in the ordinary rule in Table II are used to implement the fuzzy rule in Table II. The values of the MFs are determined by inspecting load profiles of customers confirmed as fraud by TNBD onsite inspection teams. The MFs are fine tuned by using knowledge and expertise from engineers in TNBD’s NTL Group.

IV. CONCLUSION The approach proposed in this letter extends the research work conducted in [2] by integrating human knowledge and expertise into the SVM-based FDM with the implementation of an FIS. The FIS first introduced in [4] can be used as a decision-making system for various applications. To the best of our knowledge, the work presented here is the first to use the FIS for detection of fraud and electricity theft in power distribution utilities. In this letter, the FIS acts as a postprocessing scheme to shortlist customer suspects with high probabilities of fraud and abnormalities. The FIS emulates the reasoning process that human experts (TNBD NTL Engineers) undertake in detecting fraud activities. The higher detection hitrate of the SVM-FIS proves it to be more cost effective than the FDM in [2]. With the implementation of the SVM-FIS-based FDM, TNBD’s detection hitrate will increase 12% from 60% to 72%. REFERENCES [1] A. H. Nizar and Z. Y. Dong, “Identification and detection of electricity customer behaviour irregularities,” in Proc. IEEE/Power Eng. Soc. Power Systems Conf. Expo., Seattle, WA, Mar. 15–18, 2009, pp. 1–10. [2] J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and M. Mohamad, “Nontechnical loss detection for metered customers in power utility using support vector machines,” IEEE Trans. Power Del., vol. 25, no. 2, pp. 1162–1171, Apr. 2010. [3] A. H. Nizar, Z. Y. Dong, and Y. Wang, “Power utility nontechnical loss analysis with extreme learning machine model,” IEEE Trans. Power Syst., vol. 23, no. 3, pp. 946–955, Aug. 2008. [4] J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, May/Jun. 1993.