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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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    2. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
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    4. Ahmad, Tanveer & Chen, Huanxin, 2018. "Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment," Energy, Elsevier, vol. 160(C), pages 1008-1020.
    5. Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
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    7. Netzah Calamaro & Yuval Beck & Ran Ben Melech & Doron Shmilovitz, 2021. "An Energy-Fraud Detection-System Capable of Distinguishing Frauds from Other Energy Flow Anomalies in an Urban Environment," Sustainability, MDPI, vol. 13(19), pages 1-38, September.
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    9. Yang, Kaixiang & Chen, Wuxing & Bi, Jichao & Wang, Mengzhi & Luo, Fengji, 2023. "Multi-view broad learning system for electricity theft detection," Applied Energy, Elsevier, vol. 352(C).
    10. Xuejiao Gong & Bo Tang & Ruijin Zhu & Wenlong Liao & Like Song, 2020. "Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder," Energies, MDPI, vol. 13(17), pages 1-14, August.
    11. Michał Jasiński & Tomasz Sikorski & Zbigniew Leonowicz & Klaudiusz Borkowski & Elżbieta Jasińska, 2020. "The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation," Energies, MDPI, vol. 13(9), pages 1-19, May.
    12. 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.
    13. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    14. Fernando de Souza Savian & Julio Cezar Mairesse Siluk & Tai s Bisognin Garlet & Felipe Moraes do Nascimento & Jose Renes Pinheiro & Zita Vale, 2022. "Non-technical Losses in Brazil: Overview, Challenges, and Directions for Identification and Mitigation," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 93-107, May.
    15. Miriam Benedetti & Francesca Bonfà & Vito Introna & Annalisa Santolamazza & Stefano Ubertini, 2019. "Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications," Energies, MDPI, vol. 12(20), pages 1-28, October.
    16. Xiaoquan Lu & Yu Zhou & Zhongdong Wang & Yongxian Yi & Longji Feng & Fei Wang, 2019. "Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid," Energies, MDPI, vol. 12(18), pages 1-18, September.

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