A Model for NTL Detection of Electricity Theft


  • Dr. S.Nagini
  • B. V.Kiranmayee
  • K. Jhansi Lakshmi Bai
  • . .




Non-Technical Losses NTL), k-means clustering, Preprocessing, Dissimilarity Measures..


One of the major factor effecting India’s economy is because of power consumer dishonesty. Strategies used for carrying out such activity are like illegal connections, meter tampering, billing irregularities and unpaid bills. Many countries get affected with such problem. Developed model main focus is on fraud detection at Non-Technical Losses (NTL) level. Model has been carried out with the limitations on the type of datasets available. We considered a specific spatial location in Hyderabad (Telangana State).  Analysis on such data obtained has been carried out through statistical and k-means clustering approaches. Preprocessing is carried out. The proposed model uses customer consumption data on monthly basis in terms of watts. Onsite inspection rate is reduced from the outcome derived from the model. Analysis figures out abnormal consumption behavior of a consumer.  Over years a genuine customer watt usage pattern remains the same for almost all years, with this assumption model was built. Fraudulent consumer were detected if abrupt deviations were found on the consumption pattern over years when compared on the monthly basis. The result of the fraud detection model shortlists potential fraud suspects on whom onsite inspection can be carried out and this information facilitates in reducing the Non-Technical Losses incurred by the electricity board. Electricity theft can be reduced by carrying out onsite inspection or by monitoring such consumers (on the list identified through this model). Or otherwise a clear cut strategy can be followed up by restructuring existing power system distribution or by installing tamper-proof meters that paves way for clear accountability.




[1] Oda Refou, QaisAlsafasfeh, Mohammed Alsoud “ Evaluation of Electric Energy Losses in Southern Governorates of Jordan Distribution Electric Systemâ€, IJEE 2015, 5(2):25-33, DOI:10.5923/j.ijee.20150502.02

[2] MehebubAlam, Sk Mohammed, Mandela Gain “ A Review of Losses in Distribution Sector and Minimisation Techniquesâ€, IJAREEIE, ISSN(Print):2320-3765, Vol. 3, Issue 10, October 2014.

[3] An article †Hyderabad Tops Power Theft List in AP†www.deccanchronicle.com on Jan 5th 2014

[4] An article “Telangana, Andhra Pradesh discoms’ reports under scanner†in www.deccanchronicle.com on Jan 4th 2015

[5] http://en.wikipedia.org/wiki/Theft_of_electricity

[6] http://www.forbes.com/sites/peterdetwiler/2013/04/23/electricity-theft-a-bigger-issue-than-you-think/

[7] R. Jiang, H. Tagaris, A. Lachsz, and M. Jeffrey, “Wavelet Based FeatureExtraction and Multiple Classifiers for Electricity Fraud Detection†inProc.of IEEE/PES Transmission and Distribution Conference andExhibition 2002: Asia Pacific, Vol. 3, pp. 2251-2256.

[8] C. R. Paul, “System loss in a Metropolitan utility network†IEEE PowerEngineering Journal, pp. 305-307, Sept. 1987.

[9] I. E. Davidson, A. Odubiyi, M. O. Kachienga, and B. Manhire,“Technical Loss Computation and Economic Dispatch Model in T&DSystems in a Deregulated ESI†IEEE Power Eng. Journal, Apr. 2002.

[10] www.cea.nic.in/reports/monthly/executive_rep/jan12/1-2.pdf

[11] J. W. Fourie and J. E. Calmeyer, “A statistical method to minimize electrical energy losses in a local electricity distribution network†inProc.of the 7th IEEE AFRICON Conference Africa: Technology Innovation, Gaborone, Botswana, Sept. 2004.

[12] J. R. Filho, E. M. Gontijo, A. C. Delaiba, E. Mazina, J. E. Cabral, and J.O. P. Pinto, “Fraud Identification in Electricity Company CustomersUsing Decision Trees†in Proc. of 2004 IEEE International Conferenceon Systems, Man and Cybernetics, Vol. 4, pp. 3730-3734, Oct. 2004.

[13] J. R. Galvan, A. Elices, A. Munoz, T. Czernichow, and M. A. Sanz-Bobi, “System for Detection of Abnormalities and Fraud in CustomerConsumption†in Proc. of the Electric Power Conference, Nov. 1998.

[14] A. H. Nizar, Z. Y. Dong, and J. H. Zhao, “Load Profiling and DataMining Techniques in Electricity Deregulated Market†IEEE PowerEngineering Society General Meeting, 2006, 18-22 Jun. 2006, pp. 1-7.

[15] D. Gerbec, S. Gasperic, I. Smon, and F. Gubina, “Allocation of the load profiles to consumers using probabilistic neural networks†IEEE Transaction on Power Systems, Vol. 20, No. 2, pp. 548-555, May 2005.
J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor,MI: Michigan Univ. Press, 1975 (Cambridge, MA: MIT Press, 1992)

[16] Y.-K. Kwon, B.-R.Moonand S.-D. Hong, “Critical heat flux function approximation using genetic algorithms†IEEE Transactions on NuclearScience, Vol. 52, No. 2, pp. 535-545, Apr. 2005.
V. Vapnik. Statistical Learning Theory, John Wiley & Sons, 1998.

[17] D. Wang, D. S. Yeung, E. C. C. Tsang, “Weighted Mahalanobis Distance Kernels for Support Vector Machines†IEEE Transactions onNeural Networks, Vol. 18, No. 5, pp. 1453-1462, Sept. 2007.

[18] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vectormachines. [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm

[19] †Statistical Learning with Similarity and Dissimilarity Functionsâ€vorgelegt von Dipl.-Math. Ulrike von LuxburgausTu ̈bingen, https://www.informatik.unihamburg.de/ML/contents/people/luxburg/publications/

[20] http://www.itl.nist.gov/div898/handbook/eda/section3normprpl.htm

[21] http://home.uchicago.edu/rmyerson/research/schch1.pdf

[22] https://sites.google.com/site/dataclusteringalgorithms/k-means-clustering-algorithm

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How to Cite

S.Nagini, D., V.Kiranmayee, B., Jhansi Lakshmi Bai, K., & ., . (2018). A Model for NTL Detection of Electricity Theft. International Journal of Engineering & Technology, 7(4.22), 264–268. https://doi.org/10.14419/ijet.v7i4.22.28710
Received 2019-03-31
Accepted 2019-03-31