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Review of various modeling techniques for the detection of electricity theft in smart grid environment

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  • Ahmad, Tanveer
  • Chen, Huanxin
  • Wang, Jiangyu
  • Guo, Yabin

Abstract

This review paper focuses on the various modeling practices for the identification and apprehension of non-technical losses. The modeling practices are extremely vital to develop, upsurge energy performance, examine and foresee the performance of power transmission & distribution of the electrical system. The data mining based models are innovative and have the subsistence to examine an enormous potential of energy consumption records and performing area profile for preparing housing zone directing the electricity effective living. In this concern, support vector machine model, which classifies illegal customers is a form of advanced mix evolutionary neural network model. Optimum-path forest clustering process is activated to recognize legitimate and irregular profiles of an industry as well as commercial customers to find out theft of electricity. Real time state estimation technique determines a state approximation method in the actual stage for every conversion (transformation) point. Aforementioned allows us to regulate the parts to the maximum extent of non-technical losses through the radial distribution method. The support vector machine with genetic algorithm advances a hybrid method for the non-technical loss investigation and provide an automated assistance to dominate the electricity theft. This model is simplified version of support vector machine. Decision tree and Bayesian regularization networks are appropriated to identify the several kinds of patterns of losses in the electrical system. These practices have been accompanied concerning testing and validation for power system losses in the experimental laboratory. It operates in an influence tool intended to expedite the investigators and scientists. It assists short of spending a massive amount of money, time and energy in experimental events. Prior fabrication modeling methods are remarkably significant in replication of diverse kinds of solar electrical systems. Accordingly, this study concentrates on the base of modeling methods not only saves time but, also preserves the monetary investment in the electrical system. The benefit and imminent opportunity of modeling practices are also conferred in the review paper.

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  • Ahmad, Tanveer & Chen, Huanxin & Wang, Jiangyu & Guo, Yabin, 2018. "Review of various modeling techniques for the detection of electricity theft in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2916-2933.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:2916-2933
    DOI: 10.1016/j.rser.2017.10.040
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    5. Rubén González Rodríguez & Jamer Jiménez Mares & Christian G. Quintero M., 2020. "Computational Intelligent Approaches for Non-Technical Losses Management of Electricity," Energies, MDPI, vol. 13(9), pages 1-25, May.
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