Measuring the Efficiency of Non Technical Loss inPower Utilities using Data Mining Techniques

rajesh*, Siva Sankari**
* PG Student, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamilnadu, India.
** Ph.D. Scholar, Information and Communication Engineering, Anna University, Chennai, India.
Periodicity:June - August'2014
DOI : https://doi.org/10.26634/jcom.2.2.3230

Abstract

This paper presents about Non Technical Loss (NTL) in power utilities and it describes how to handle it. Non-technical Loss has been an influential factor in the benefits of electric power utilities. At the same time, to distribute generation extensively installed, the consumption patterns are having many similarities between dishonest users and normal users. Non Technical Loss may be theft of electricity, illegal connection, fault metering and billing error. Improving the reliability of the NTL detection algorithm becomes particularly important. Data mining techniques are used to detect the Non Technical Loss using classification algorithm. The implementation is to build an intelligent computational tool to identify the non-technical losses and to select its most close feature, considering information from the database with consumer profiles.

Keywords

Multilayer Perceptron, Data mining, Non-Technical Loss (NTL).

How to Cite this Article?

Rajesh, R., Sankari. S. (2014). Measuring the Efficiency of Non Technical Loss in Power Utilities Using Data Mining Techniques. i-manager’s Journal on Computer Science, 2(2), 19-24. https://doi.org/10.26634/jcom.2.2.3230

References

[1]. J. Nagi, A. Mohammad, K. Yap, S. Tiong, and S. Ahmed, (2008). “Non technical loss analysis for detection of electricity theft using support vector machines,” in nd Proceedings of the 2 IEEE International Power and Energy Conference, pp. 907–912.
[2]. K. S. Yap, Z. Hussien, and A. Mohamad, (2007). “Abnormalities and fraudelectric meter detection using hybrid support vector machine and genetic algorithm,” in Proc. 3rd IASTED Int. Conf. Advances in Computer Science and Technology, Phuket, Thailand.
[3]. Kim, M, and Kim T., (2002). "A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection", Proceedings of IDEAL., pp.378-383.
[4]. Carlos León, Félix Biscarri, Iñigo Monedero, Juan Ignacio Guerrero, Jesús Biscarri, and Rocío Millán, (2011). “Variability and Trend-Based Generalized Rule Induction Model to NTL Detection in Power Companies” IEEE Trasaction on Power system, Vol. 26(4), pp.1798-1807.
[5]. A. H. Nizar, Z. Y. Dong, J. H. Zhao, and P. Zhang, (2007). “A Data Mining Based NTL Analysis Method”, IEEE Power Engineering Society (PES) General Meeting, pp.1-8.
[6]. M. Kantardzic, (2003). “Data Mining: Concepts, Models, Methods, and Algorithms”, Hoboken, NJ: Wiley- Interscience: IEEE Press, pp.1-12.
[7]. T. B. Smith, (2004). “Electricity Theft: A Comparative Analysis”, Energy Policy, Vol.32(18), pp.2067-2076.
[8]. J. Filho, (2004). “Fraud identification in electricity company costumers using decision tree,” in Proc. IEEE/PES Int. Conf. Systems, Man and Cybernetics, The Hague, The Netherlands, Vol.4.
[9]. NanlinJin, Peter Flach, Tom Wilcox, Royston Sellman, JoshuaThumim, Arno Knobbe, (2014). “Subgroup Discovery in Smart Electricity Meter Data”, IEEE Transaction on Industrial Informatics, Vol.10(2).
[10]. A. H. Nizar, Z. Y. Dong, M. Jalaluddin, and M. J. Raffles, (2006). “Load Profiling Non-Technical Loss st Activities in a Power Utility”, in Proc. of the 1 International Power and Energy Conference (PECON), Putrajaya, Malaysia.
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