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Review Articles

Detection of Power Theft in Low Voltage Distribution Systems: A Review from the Indian Perspective

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References

  • C. E. Authority “Annual report (2018–19) central electricity authority (Tech. rep.),” Ministry of Power, Govt. of India, 2018.
  • P. F. C. Ltd., “The performance of state power utilities for the years 2013–14 to 2015–16 (Tech. rep.),” Power Finance Corporation Ltd., Govt. of India undertaking, 2018.
  • S. Bhattacharya and U. R. Patel, “Does the exuberance in the Indian power sector have legs?,” Global Econom. Develop. Working Paper, Vol. 45, 2011.
  • Power and E. Division, “Annual report (2017–18) on the working of State Power Utilities and Electricity Departments Energy (Tech. rep.),” Planning Commission, Govt. of India, 2018.
  • Power and E. Division, “Annual report (2016–17) on the working of State Power Utilities and Electricity Departments Energy (Tech. rep.),” Planning Commission, Govt. of India, 2017.
  • Power and E. Division, “Annual report (2015–16) on the working of State Power Utilities and Electricity Departments Energy (Tech. rep.),” Planning Commission, Govt. of India, 2016.
  • Power and E. Division, “Annual report (2014–15) on the working of State Power Utilities and Electricity Departments Energy (Tech. rep.),” Planning Commission, Govt. of India, 2015.
  • K. L. Joseph, “The politics of power: electricity reform in India,” Energy. Policy., Vol. 38, no. 1, pp. 503–511, 2010. doi: 10.1016/j.enpol.2009.09.041
  • G. Sreenivasan, Power theft: educates and sesitises people about the menace of poer theft, 4th ed. PHI Learning Private Limited, 2017.
  • F. Wang, F. Yang, T. Liu, and X. Hu, “Measuring energy meter of three-phase electricity-stealing defense system,” in Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on, IEEE, 2011, pp. 11–15.
  • A. K. Gupta, A. Mukherjee, A. Routray, and R. Biswas, “A novel power theft detection algorithm for low voltage distribution network,” in IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2017, pp. 3603–3608.
  • M. of power, Guidelines for Specifications Energy Efficient Outdoor Type Three Phase and Single Phase Distribution Transformer, Aug. 2008.
  • R. Singh, and A. Singh, “Causes of failure of distribution transformers in india,” in 2010 9th International Conference on Environment and Electrical Engineering, IEEE, 2010, pp. 388–391.
  • V. Gaur and E. Gupta, “The determinants of electricity theft: an empirical analysis of Indian states,” Energy. Policy., Vol. 93, pp. 127–136, 2016. doi: 10.1016/j.enpol.2016.02.048
  • T. K. Mideksa and S. Kallbekken, “The impact of climate change on the electricity market: A review,” Energy. Policy., Vol. 38, no. 7, pp. 3579–3585, 2010. doi: 10.1016/j.enpol.2010.02.035
  • M. A. Golden and B. Min, Theft and loss of electricity in an Indian state. International Growth Centre, 2012.
  • M. of Law and Justice, “The Electricity Act, 2003 (Tech. rep.,)” Planning Commission, Govt. of India, May 26, 2003.
  • B. Ram, Power Theft, Nov. 02, 2018.
  • M.-Y. Kim, D. Kim, B. Lee, T. H. Ahn, and B. Bat, “Extended smart meters-based remote detection method for illegal electricity usage,” IET Generation Trans. Distribut., Vol. 7, no. 11, pp. 1332–1343, 2013. doi: 10.1049/iet-gtd.2012.0287
  • B. Khoo and Y. Cheng, “Using rfid for anti-theft in a Chinese electrical supply company: A cost–benefit analysis,” in 2011 Wireless Telecommunications Symposium (WTS), IEEE, 2011, pp. 1–6.
  • A. V. Christopher, G. Swaminathan, M. Subramanian, and P. Thangaraj, “Distribution line monitoring system for the detection of power theft using power line communication,” in 2014 IEEE Conference on Energy Conversion (CENCON), IEEE, 2014, pp. 55–60.
  • Y. Tang, C.-W. Ten, and K. P. Schneider, “Inference of tampered smart meters with validations from feeder-level power injections,” in 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), IEEE, 2019, pp.2783–2788.
  • M. U. Hashmi and J. G. Priolkar, “Anti-theft energy metering for smart electrical distribution system,” in Industrial Instrumentation and Control (ICIC), 2015 International Conference on, IEEE, 2015, pp. 1424–1428.
  • I. H. Cavdar, “A solution to remote detection of illegal electricity usage via power line communications,” IEEE Trans. Power Del., Vol. 19, no. 4, pp. 1663–1667, 2004. doi: 10.1109/TPWRD.2003.822540
  • P. J. Van Rensburg and H. C. Ferreira, “Design of a bidirectional impedance-adapting transformer coupling circuit for low-voltage power-line communications,” IEEE Trans. Power Delivery, Vol. 20, no. 1, pp. 64–70, 2005. doi: 10.1109/TPWRD.2004.835260
  • A. Pasdar and S. Mirzakuchaki, “A solution to remote detecting of illegal electricity usage based on smart metering,” in Soft Computing Applications, 2007. SOFA 2007. 2nd International Workshop on, IEEE, 2007, pp. 163–167.
  • B. Bat-Erdene, S.-Y. Nam and D.-H. Kim, “A novel remote detection method of illegal electricity usage based on smart resistance,” Future Information Technology, 2011, pp. 214–223.
  • S. McLaughlin, B. Holbert, S. Zonouz, and R. Berthier, “Amids: A multi-sensor energy theft detection framework for advanced metering infrastructures,” in Smart Grid Communications (SmartGridComm), 2012 IEEE Third International Conference on, IEEE, 2012, pp. 354–359.
  • S. McLaughlin, B. Holbert, A. Fawaz, R. Berthier, and S. Zonouz, “A multi-sensor energy theft detection framework for advanced metering infrastructures,” IEEE J. Selected Areas Commun., Vol. 31, no. 7, pp. 1319–1330, 2013. doi: 10.1109/JSAC.2013.130714
  • A. Khazaee, H. H. Safa, M. Ghasempour, and H. Delavari, “Distribution loss reduction in residential and commercial pilots by using ami system,” CIRED-Open Access Proc. J., Vol. 2017, no. 1, pp. 1711–1714, 2017. doi: 10.1049/oap-cired.2017.0500
  • S. S. S. R. Depuru, L. Wang, and V. Devabhaktuni, “Electricity theft: overview, issues, prevention and a smart meter based approach to control theft,” Energy Policy, Vol. 39, no. 2, pp. 1007–1015, 2011. doi: 10.1016/j.enpol.2010.11.037
  • M. Veillette, “Process for detecting energy theft,” Apr. 21 2015. US Patent 9,013,173.
  • A. Ansari, “Systems, methods, and apparatus for detecting theft and status of electrical power,” Aug. 26 2014. US Patent 8,818,742.
  • Y. Tawaragi, “Power theft inspection apparatus and method, and recording medium,” Jan. 9 2015. US Patent App. 14/593,160.
  • S. Deb, P. K. Bhowmik, and A. Paul, “Remote detection of illegal electricity usage employing smart energy meter-a current based technique,” in ISGT2011-India, IEEE, 2011, pp. 391–395.
  • N. Mohammad, A. Barua, and M. A. Arafat, “A smart prepaid energy metering system to control electricity theft,” in 2013 International Conference on Power, Energy and Control (ICPEC), IEEE, 2013, pp. 562–565.
  • L. Fucun, G. Hongxia, L. Lijun, W. Zhelong, and W. Peng, “Anti-theft plug-in metering device and its method based on interlock-delay,” in 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), IEEE, 2015, pp. 651–654.
  • M. Tariq and H. V. Poor, “Electricity theft detection and localization in grid-tied microgrids,” IEEE. Trans. Smart. Grid., Vol. 9, no. 3, pp. 1920–1929, 2016.
  • S. A. Salinas and P. Li, “Privacy-preserving energy theft detection in microgrids: A state estimation approach,” IEEE Trans. Power Syst., Vol. 31, no. 2, pp. 883–894, 2016. doi: 10.1109/TPWRS.2015.2406311
  • S.-C. Huang, Y.-L. Lo and C.-N. Lu, “Non-technical loss detection using state estimation and analysis of variance,” IEEE Trans. Power Syst., Vol. 28, no. 3, pp. 2959–2966, 2013. doi: 10.1109/TPWRS.2012.2224891
  • P. Jokar, N. Arianpoo, and V. C. Leung, “Electricity theft detection in ami using customers' consumption patterns,” IEEE. Trans. Smart. Grid., Vol. 7, no. 1, pp. 216–226, 2015. doi: 10.1109/TSG.2015.2425222
  • P. Glauner, J. A. Meira, P. Valtchev, R. State, and F. Bettinger, “The challenge of non-technical loss detection using artificial intelligence: A survey,” arXiv preprint arXiv:1606.00626, 2016.
  • S. Salinas, M. Li, and P. Li, “Privacy-preserving energy theft detection in smart grids: A p2p computing approach,” IEEE J. Selected Areas Commun., Vol. 31, no. 9, pp. 257–267, 2013. doi: 10.1109/JSAC.2013.SUP.0513023
  • J. L. Viegas, P. R. Esteves, R. Melício, V. Mendes, and S. M. Vieira, “Solutions for detection of non-technical losses in the electricity grid: A review,” Renew. Sustain. Energy Rev., Vol. 80, pp. 1256–1268, 2017. doi: 10.1016/j.rser.2017.05.193
  • Z. Qu, H. Li, Y. Wang, J. Zhang, A. Abu-Siada, and Y. Yao, “Detection of electricity theft behavior based on improved synthetic minority oversampling technique and random forest classifier,” Energies, Vol. 13, no. 8, pp. 2039, 2020. doi: 10.3390/en13082039
  • P. Massaferro, J. M. Di Martino, and A. Fernández, “Fraud detection in electric power distribution: An approach that maximizes the economic return,” IEEE Transactions on Power Systems, 2019.
  • S. Jain, K. A. Choksi, and N. M. Pindoriya, “Rule-based classification of energy theft and anomalies in consumers load demand profile,” IET Smart Grid, Vol. 2, no. 4, pp. 612–624, 2019. doi: 10.1049/iet-stg.2019.0081
  • K. M. Ghori, R. A. Abbasi, M. Awais, M. Imran, A. Ullah, and L. Szathmary, “Performance analysis of different types of machine learning classifiers for non-technical loss detection,” IEEE Access., Vol. 8, pp. 16033–16048, 2019. doi: 10.1109/ACCESS.2019.2962510
  • I. Monedero, F. Biscarri, C. León, J. I. Guerrero, J. Biscarri, and R. Millán, “Detection of frauds and other non-technical losses in a power utility using Pearson coefficient, Bayesian networks and decision trees,” Inter. J. Electr. Power Energy Syst., Vol. 34, no. 1, pp. 90–98, 2012. doi: 10.1016/j.ijepes.2011.09.009
  • M. Ismail, M. F. Shaaban, M. Naidu, and E. Serpedin, “Deep learning detection of electricity theft cyber-attacks in renewable distributed generation,” IEEE Transactions on Smart Grid, 2020.
  • W. Zhang, X. Dong, H. Li, J. Xu, and D. Wang, “Unsupervised detection of abnormal electricity consumption behavior based on feature engineering,” IEEE Access., Vol. 8, pp. 55483–55500, 2020. doi: 10.1109/ACCESS.2020.2980079
  • T. Hu, Q. Guo, X. Shen, H. Sun, R. Wu, and H. Xi, “Utilizing unlabeled data to detect electricity fraud in ami: A semisupervised deep learning approach,” IEEE Transactions on Neural Networks and Learning Systems, 2019.
  • G. Micheli, E. Soda, M. T. Vespucci, M. Gobbi, and A. Bertani, “Big data analytics: an aid to detection of non-technical losses in power utilities,” Comput. Management Sci., Vol. 16, no. 1-2, pp. 329–343, 2019. doi: 10.1007/s10287-018-0325-x
  • Y. Gao, B. Foggo, and N. Yu, “A physically inspired data-driven model for electricity theft detection with smart meter data,” IEEE Trans. Indust. Inform., Vol. 15, no. 9, pp. 5076–5088, 2019. doi: 10.1109/TII.2019.2898171
  • X. Wang, I. Yang, and S.-H. Ahn, “Sample efficient home power anomaly detection in real time using semi-supervised learning,” IEEE Access., Vol. 7, pp. 139712–139725, 2019.
  • X. Wang, T. Zhao, H. Liu, and R. He, “Power consumption predicting and anomaly detection based on long short-term memory neural network,” in 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), IEEE, 2019, pp. 487–491.
  • C.-H. Lin, S.-J. Chen, C.-L. Kuo, and J.-L. Chen, “Non-cooperative game model applied to an advanced metering infrastructure for non-technical loss screening in micro-distribution systems,” IEEE Trans. Smart. Grid., Vol. 5, no. 5, pp. 2468–2469, 2014. doi: 10.1109/TSG.2014.2327809
  • Y. Liu and S. Hu, “Cyberthreat analysis and detection for energy theft in social networking of smart homes,” IEEE Trans. Comput. Soc. Syst., Vol. 2, no. 4, pp. 148–158, 2015. doi: 10.1109/TCSS.2016.2519506
  • C. Bandim, J. Alves, A. Pinto, F. Souza, M. Loureiro, C. Magalhaes, and F. Galvez-Durand, “Identification of energy theft and tampered meters using a central observer meter: A mathematical approach,” in Transmission and Distribution Conference and Exposition, 2003 IEEE PES, vol. 1, IEEE, 2003, pp. 163–168.
  • 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 Delivery, Vol. 25, no. 2, pp. 1162–1171, 2009. doi: 10.1109/TPWRD.2009.2030890
  • R. Jiang, R. Lu, Y. Wang, J. Luo, C. Shen, and X. Shen, “Energy-theft detection issues for advanced metering infrastructure in smart grid,” Tsinghua Sci. Technol., Vol. 19, no. 2, pp. 105–120, 2014. doi: 10.1109/TST.2014.6787363
  • L. Chen, X. Xu, and C. Wang, “Research on anti-electricity stealing method base on state estimation,” in 2011 IEEE Power Engineering and Automation Conference, Vol. 2, IEEE, 2011, pp. 413–416.
  • D. N. Nikovski, Z. Wang, A. Esenther, H. Sun, K. Sugiura, T. Muso, and K. Tsuru, “Smart meter data analysis for power theft detection,” in International Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, 2013, pp. 379–389.
  • S. Sahoo, D. Nikovski, T. Muso, and K. Tsuru, “Electricity theft detection using smart meter data,” in Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, IEEE, 2015, pp. 1–5.
  • V. B. Krishna, C. A. Gunter, and W. H. Sanders, “Evaluating detectors on optimal attack vectors that enable electricity theft and der fraud,” IEEE J. Sel. Top. Signal. Process., Vol. 12, no. 4, pp. 790–805, 2018. doi: 10.1109/JSTSP.2018.2833749
  • Y. Zhou, X. Chen, A. Y. Zomaya, L. Wang, and S. Hu, “A dynamic programming algorithm for leveraging probabilistic detection of energy theft in smart home,” IEEE Trans. Emerg. Top. Comput., Vol. 3, no. 4, pp. 502–513, 2015. doi: 10.1109/TETC.2015.2484841
  • B. Dangar and S. Joshi, “Notice of violation of IEEE publication principles electricity theft detection techniques for metered power consumer in Guvnl, Gujarat, India,” in 2015 Clemson University Power Systems Conference (PSC), IEEE, 2015, pp. 1–6.
  • R. Wu, L. Wang, and T. Hu, “Adaboost-svm for electrical theft detection and grnn for stealing time periods identification,” in IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2018, pp. 3073–3078.
  • Y. Zhou, Y. Liu, and S. Hu, “Energy theft detection in multi-tenant data centers with digital protective relay deployment,” IEEE Trans. Sustain. Comput., Vol. 3, no. 1, pp. 16–29, 2017. doi: 10.1109/TSUSC.2017.2705192
  • S. K. Singh, R. Bose, and A. Joshi, “Entropy-based electricity theft detection in ami network,” IET Cyber-Physical Syst. Theory Appl., Vol. 3, no. 2, pp. 99–105, 2018. doi: 10.1049/iet-cps.2017.0063
  • N. F. Avila, G. Figueroa, and C.-C. Chu, “Ntl detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting,” IEEE Trans. Power Syst., Vol. 33, no. 6, pp. 7171–7180, 2018. doi: 10.1109/TPWRS.2018.2853162
  • J. B. Leite and J. R. S. Mantovani, “Detecting and locating non-technical losses in modern distribution networks,” IEEE Trans. Smart. Grid., Vol. 9, no. 2, pp. 1023–1032, 2016. doi: 10.1109/TSG.2016.2574714
  • E. W. S. Angelos, O. R. Saavedra, O. A. C. Cortés, and A. N. de Souza, “Detection and identification of abnormalities in customer consumptions in power distribution systems,” IEEE Trans. Power Delivery, Vol. 26, no. 4, pp. 2436–2442, 2011. doi: 10.1109/TPWRD.2011.2161621
  • C. C. Ramos, D. Rodrigues, A. N. de Souza, and J. P. Papa, “On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization,” IEEE Trans. Smart. Grid., Vol. 9, no. 2, pp. 676–683, 2016. doi: 10.1109/TSG.2016.2560801
  • D. Rodrigues, C. C. O. Ramos, A. N. De Souza, and J. P. Papa, “Black hole algorithm for non-technical losses characterization,” in 2015 IEEE 6th Latin American Symposium on Circuits & Systems (LASCAS), IEEE, 2015, pp. 1–4.
  • B. Min and M. Golden, “Electoral cycles in electricity losses in india,” Energy Policy., Vol. 65, pp. 619–625, 2014. doi: 10.1016/j.enpol.2013.09.060
  • X. Xia, Y. Xiao, and W. Liang, “ABSI: an adaptive binary splitting algorithm for malicious meter inspection in smart grid,” IEEE Trans. Inform. Forensics Security, Vol. 14, no. 2, pp. 445–458, 2018. doi: 10.1109/TIFS.2018.2854703
  • R. Kappagantu and S. A. Daniel, “Challenges and issues of smart grid implementation: A case of Indian scenario,” J. Electr. Syst. Inform. Technol., Vol. 5, no. 3, pp. 453–467, 2018. doi: 10.1016/j.jesit.2018.01.002
  • A. O. Otuoze, M. W. Mustafa, and R. M. Larik, “Smart grids security challenges: classification by sources of threats,” J. Electr. Syst. Inform. Technol., Vol. 5, no. 3, pp. 468–483, 2018. doi: 10.1016/j.jesit.2018.01.001
  • F. Xiao and Q. Ai, “Electricity theft detection in smart grid using random matrix theory,” IET Generat. Trans. Distribut., Vol. 12, no. 2, pp. 371–378, 2017. doi: 10.1049/iet-gtd.2017.0898
  • N. Uribe-Pérez, L. Hernández, D. De la Vega, and I. Angulo, “State of the art and trends review of smart metering in electricity grids,” Appl. Sci., Vol. 6, no. 3, pp. 68, 2016. doi: 10.3390/app6030068
  • A. Jindal, A. Dua, K. Kaur, M. Singh, N. Kumar, and S. Mishra, “Decision tree and svm-based data analytics for theft detection in smart grid,” IEEE Trans. Industr. Inform., Vol. 12, no. 3, pp. 1005–1016, 2016. doi: 10.1109/TII.2016.2543145
  • A. Yassine, A. A. N. Shirehjini, and S. Shirmohammadi, “Smart meters big data: game theoretic model for fair data sharing in deregulated smart grids,” IEEE Access., Vol. 3, pp. 2743–2754, 2015. doi: 10.1109/ACCESS.2015.2504503
  • Z. Xiao, Y. Xiao, and D. H. -C. Du, “Exploring malicious meter inspection in neighborhood area smart grids,” IEEE Trans. Smart. Grid., Vol. 4, no. 1, pp. 214–226, 2012. doi: 10.1109/TSG.2012.2229397
  • C.-H. Lo and N. Ansari, “Consumer: A novel hybrid intrusion detection system for distribution networks in smart grid,” IEEE Trans. Emerg. Top. Comput., Vol. 1, no. 1, pp. 33–44, 2013. doi: 10.1109/TETC.2013.2274043
  • S. S. S. R. Depuru, L. Wang, V. Devabhaktuni, and N. Gudi, “Smart meters for power grid–challenges, issues, advantages and status,” in 2011 IEEE/PES Power Systems Conference and Exposition, IEEE, 2011, pp. 1–7.
  • S. Weckx, C. Gonzalez, J. Tant, T. De Rybel, and J. Driesen, “Parameter identification of unknown radial grids for theft detection,” in 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), IEEE, 2012, pp. 1–6.
  • G. Fenza, M. Gallo, and V. Loia, “Drift-aware methodology for anomaly detection in smart grid,” IEEE Access., Vol. 7, pp. 9645–9657, 2019. doi: 10.1109/ACCESS.2019.2891315
  • Z. Zheng, Y. Yang, X. Niu, H.-N. Dai, and Y. Zhou, “Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids,” IEEE Trans. Industr. Inform., Vol. 14, no. 4, pp. 1606–1615, 2017. doi: 10.1109/TII.2017.2785963
  • X. Xia, Y. Xiao, W. Liang, and M. Zheng, “Gthi: A heuristic algorithm to detect malicious users in smart grids,” IEEE Transactions on Network Science and Engineering, 2018.
  • T. B. Smith, “Electricity theft: a comparative analysis,” Energy. Policy., Vol. 32, no. 18, pp. 2067–2076, 2004. doi: 10.1016/S0301-4215(03)00182-4
  • P. Mishra, Overview of Power Sector, Sept. 2019.
  • I. Jha, S. Sen, and K. Bhambhani, “Improvement of power distribution system–a few aspects,” National Power Systems Conference, NPSC, Vol. Indian Institute of Technology Kharagpur, India, Dec. 27–29, 2002.
  • J. R. Agüero, “Improving the efficiency of power distribution systems through technical and non-technical losses reduction,” in PES T&D 2012, IEEE, 2012, pp. 1–8.
  • S. S. S. Depuru, L. Wang, V. Devabhaktuni, and N. Gudi, “Measures and setbacks for controlling electricity theft,” in North American Power Symposium 2010, IEEE, 2010, pp. 1–8.
  • D. Minoli, Building the internet of things with IPv6 and MIPv6: the evolving world of m2m communications. John Wiley & Sons, 2013.
  • T. Whiffen, S. Naylor, J. Hill, L. Smith, P. Callan, M. Gillott, C. Wood, and S. Riffat, “A concept review of power line communication in building energy management systems for the small to medium sized non-domestic built environment,” Renew. Sustain. Energy Rev., Vol. 64, pp. 618–633, 2016. doi: 10.1016/j.rser.2016.06.069
  • R. R. Mohassel, A. Fung, F. Mohammadi, and K. Raahemifar, “A survey on advanced metering infrastructure,” Inter. J. Electr. Power Energy Syst., Vol. 63, pp. 473–484, 2014. doi: 10.1016/j.ijepes.2014.06.025
  • J. Nagy, J. Oláh, E. Erdei, D. Máté, and J. Popp, “The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain–the case of Hungary,” Sustainability, Vol. 10, no. 10, pp. 3491, 2018. doi: 10.3390/su10103491
  • R. J. Tom, S. Sankaranarayanan, and J. J. Rodrigues, “Agent negotiation in an iot-fog based power distribution system for demand reduction,” Sustain. Energy Technol. Assessments, Vol. 38, pp. 100653, 2020.
  • W. Li, T. Logenthiran, V.-T. Phan, and W. L. Woo, “A novel smart energy theft system (sets) for iot-based smart home,” IEEE Inter. Things J., Vol. 6, no. 3, pp. 5531–5539, 2019. doi: 10.1109/JIOT.2019.2903281
  • A. Ali, G. A. Shah, M. O. Farooq, and U. Ghani, “Technologies and challenges in developing machine-to-machine applications: A survey,” J. Netw. Comput. Appl., Vol. 83, pp. 124–139, 2017. doi: 10.1016/j.jnca.2017.02.002
  • M. Khan, Uttar Pradesh Government Extends OTS Registration Date to February 15, February 3, 2019.
  • G. Manager, Brihan Mumbai Electric Supply and Transport Undertaking, Administrative Order No: 404.
  • A. Roy, Inform about power theft get 10% reward, August 27, 2018.
  • V. Rai, R. Tongia, G. Shrimali, and N. Abhyankar, “Data for development: the case for an Indian Energy Information Administration,” Energy Res. Soc. Sci., Vol. 25, pp. 105–109, 2017. doi: 10.1016/j.erss.2017.01.002
  • I. H. Cavdar, “A solution to remote detection of illegal electricity usage via power line communications,” IEEE Trans. Power Delivery, Vol. 19, no. 4, pp. 1663–1667, 2004. doi: 10.1109/TPWRD.2003.822540
  • O. M. Komolafe and K. M. Udofia, “A technique for electrical energy theft detection and location in low voltage power distribution systems,” Engin. Appl. Sci., Vol. 5, no. 2, pp. 41, 2020.
  • J. E. Mendiola and M. A. A. Pedrasa, “Detection of pilferage in an ami-enabled low-voltage network using energy reading anomalies,” in 2019 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), IEEE, 2019, pp. 1–6.
  • M. Aryanezhad, “A novel approach to detection and prevention of electricity pilferage over power distribution network,” Inter. J. Electr. Power Energy Syst., Vol. 111, pp. 191–200, 2019. doi: 10.1016/j.ijepes.2019.04.005
  • B. Bat-Erdene, B. Lee, M.-Y. Kim, T. H. Ahn, and D. Kim, “Extended smart meters-based remote detection method for illegal electricity usage,” IET Generation Trans. Distribut., Vol. 7, no. 11, pp. 1332–1343, 2013. doi: 10.1049/iet-gtd.2012.0287
  • G. Sella, et al., “Theft detection and prevention in a power generation system,” Feb. 3 2015. US Patent 8,947,194.
  • R. Jiang, R. Lu, Y. Wang, J. Luo, C. Shen, and X. S. Shen, “Energy-theft detection issues for advanced metering infrastructure in smart grid,” Tsinghua Sci. Technol., Vol. 19, no. 2, pp. 105–120, 2014. doi: 10.1109/TST.2014.6787363
  • R. Punmiya and S. Choe, “Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing,” IEEE. Trans. Smart. Grid., Vol. 10, no. 2, pp. 2326–2329, 2019. doi: 10.1109/TSG.2019.2892595
  • T. Ferreira, F. Trindade, and J. Vieira, “Load flow-based method for nontechnical electrical loss detection and location in distribution systems using smart meters,” IEEE Trans. Power Syst., 2020.
  • P. Glauner, A. Boechat, L. Dolberg, R. State, F. Bettinger, Y. Rangoni, and D. Duarte, “Large-scale detection of non-technical losses in imbalanced data sets,” in 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), IEEE, 2016, pp. 1–5.
  • H. Qin, H. Zhou, and J. Cao, “Imbalanced learning algorithm based intelligent abnormal electricity consumption detection,” Neurocomputing, 2020.
  • A. A. Imayakumar, A. Dubey, and A. Bose, “Anomaly detection for primary distribution system measurements using principal component analysis,” in 2020 IEEE Texas Power and Energy Conference (TPEC), IEEE, 2020, pp. 1–6.
  • S. K. Singh, R. Bose, and A. Joshi, “Energy theft detection for ami using principal component analysis based reconstructed data,” IET Cyber-Phys. Syst. Theory Appl., Vol. 4, no. 2, pp. 179–185, 2019. doi: 10.1049/iet-cps.2018.5050
  • S. Bashkari, A. Sami, and M. Rastegar, “Outage cause detection in power distribution systems based on data mining,” IEEE Trans. Ind. Inform., 2020.
  • M. A. de Souza, J. L. Pereira, G. d. O. Alves, B. C. de Oliveira, I. D. Melo, and P. A. Garcia, “Detection and identification of energy theft in advanced metering infrastructures,” Electr. Power Syst. Res., Vol. 182, pp. 106258, 2020. doi: 10.1016/j.epsr.2020.106258
  • Brightsandz, Extremely Low Frequency (ELF) Radiation – The Essential Guide, April 17, 2019.
  • Sarawak, Police Diffuse Ugly Scene During Sesco's Meter Inspection, Jan. 13, 2013.
  • Y. Singh, Bhiwani District Leads in Power Theft, Report Presented in the House, Dec. 2, 2015.

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