Abstract
Electricity theft is a major concern for power distribution utilities. The increase in non-technical losses give rise to imbalance between electricity supply and demand resulting into overloading of existing distribution network, reduction in reliability and stability of supply and additional tariff posed on genuine consumers. Although, the smart metering systems has resolved meter related power theft problems, however, direct tapping on distribution line remains perpetual issue which should be stringently annihilated. Thus, this paper presents real-time electricity theft detection using energy consumption data of all legal consumers and outgoing distribution transformer energy meter data. In order to prevent the hook-line activity, a fuzzy inference based scheme is implemented in LabVIEW to operate electricity theft prevention system (ETPS). The ETPS develops unsuitable voltage across illegal consumer and hinders normal operation of their appliances. The consumer care unit (CCU) interlocked with ETPS maintains normal supply voltage at legal consumers end. The suitability, flexibility in operation and effectiveness of the proposed ETPS and CCU based theft prevention scheme is experimentally and practically demonstrated as case study under various voltage regulation and energy loss scenarios.
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References
Palit D, Bandyopadhyay KR (2017) Rural electricity access in india in retrospect: a critical rumination. Energy Policy 109:109–120
Depuru SSSR, Wang L, Devabhaktuni V (2011) Electricity theft: overview, issues, prevention and a smart meter based approach to control theft. Energy Policy 39(2):1007–1015
Sharma T, Pandey K, Punia D, Rao J (2016) Of pilferers and poachers: combating electricity theft in india. Energy Res Soc Sci 11:40–52
Viegas JL, Esteves PR, Melício R, Mendes V, Vieira SM (2017) Solutions for detection of non-technical losses in the electricity grid: a review. Renew Sustain Energy Rev 80:1256–1268
Depuru SSSR, Wang L, Devabhaktuni V, Gudi N (2010) Measures and setbacks for controlling electricity theft, in North American power symposium. Sept 2010:1–8
Sreenivasan G (2016) Power Theft. PHI Learning Pvt. Ltd, Delhi, New Delhi
Fucun L, Hongxia G, Lijun L, Zhelong W, Peng W (2015) Anti-theft plug-in metering device and its method based on interlock-delay, In: Instrumentation and measurement, computer, communication and control (IMCCC), 2015 Fifth international conference on. IEEE, pp 651–654
Mohammad N, Barua A, Arafat MA (2013) A smart prepaid energy metering system to control electricity theft, In: Power, energy and control (ICPEC), 2013 international conference on. IEEE, pp 562–565
Tan S, De D, Song WZ, Yang J, Das SK (2017) Survey of security advances in smart grid: a data driven approach. IEEE Commun Surv Tutor 19(1):397–422 (Firstquarter)
Yurtseven Ç (2015) The causes of electricity theft: an econometric analysis of the case of Turkey. Utilities Policy 37:70–78
Jamil F (2013) On the electricity shortage, price and electricity theft nexus. Energy Policy 54:267–272
Chakraborty M (2018) Advanced monitoring based intrusion detectio system for distributed and intelligent energy theft: DIET attack in advanced metering infrastructure. In: Gavrilova M, Tan C, Chaki N, Saeed K (eds) Transactions on computational science XXXI. Lecture Notes in Computer Science, vol 10730. Berlin, Heidelberg, pp 77–97. https://doi.org/10.1007/978-3-662-56499-8_5
Arango LG, Deccache E, Bonatto BD, Arango H, Pamplona E (2017) Study of electricity theft impact on the economy of a regulated electricity company. J Control Autom Electr Syst 28(4):567–575
Nikovski DN, Wang Z, Esenther A, Sun H, Sugiura K, Muso T, Tsuru K (2013) Smart meter data analysis for power theft detection. In: International workshop on machine learning and data mining in pattern recognition. Springer, pp 379–389
Dou J, Aliaosha Y (2018) Optimization method of suspected electricity theft topic model based on chi-square test and logistic regression. In: International conference of pioneering computer scientists, engineers and educators. Springer, pp 389–400
Jiang R, Lu R, Wang Y, Luo J, Shen C, Shen XS (2014) Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci Technol 19(2):105–120
Gao Y, Foggo B, Yu N (2019) A physically inspired data-driven model for electricity theft detection with smart meter data. IEEE Trans Industrial Inform 15(9):5076–5088
Tao J, Michailidis G (2019) A statistical framework for detecting electricity theft activities in smart grid distribution networks. IEEE J Sel Areas Commun 18(1):205–216
Amin S, Schwartz GA, Cárdenas AA, Sastry SS (2015) Game-theoretic models of electricity theft detection in smart utility networks: providing new capabilities with advanced metering infrastructure. IEEE Control Syst Mag 35(1):66–81
Guerrero JI, León C, Monedero I, Biscarri F, Biscarri J (2014) Improving knowledge-based systems with statistical techniques, text mining, and neural networks for non-technical loss detection. Knowl Based Syst 71:376–388
Bandim C, Alves J, Pinto A, Souza F, Loureiro M, Magalhaes C, Galvez-Durand F, Identification of energy theft and tampered meters using a central observer meter: a mathematical approach. In: (2003) IEEE PES transmission and distribution conference and exposition (IEEE Cat. No. 03CH37495), vol. 1. IEEE 2003, pp 163–168
Ghori KM, Imran M, Nawaz A, Abbasi RA, Ullah A, Szathmary L (2020) Performance analysis of machine learning classifiers for non-technical loss detection. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01649-9
Nagi J, Yap KS, Tiong SK, Ahmed SK, Mohammad A (2008) Detection of abnormalities and electricity theft using genetic support vector machines. In: TENCON 2008–2008 IEEE region 10 conference. IEEE 1–6
Ahmad S, Baig Z (2012) Fuzzy-based optimization for effective detection of smart grid cyber-attacks. Int J Smart Grid Clean Energy 1(1):15–21
Nabil M, Ismail M, Mahmoud M, Shahin M, Qaraqe K, Serpedin E (2019) Deep learning-based detection of electricity theft cyber-attacks in smart grid AMI networks. In: Alazab M, Tang M (eds) Deep learning applications for cyber security. Advanced sciences and technologies for security applications. Springer, Cham, pp 73–102. https://doi.org/10.1007/978-3-030-13057-2_4
Li B, Xu K, Cui X, Wang Y, Ai X, Wang Y (2018) Multi-scale densenet-based electricity theft detection. In: International conference on intelligent computing. Springer, pp 172–182
Razavi R, Gharipour A, Fleury M, Akpan IJ (2019) A practical feature-engineering framework for electricity theft detection in smart grids. Appl Energy 238:481–494
Ganguly P, Nasipuri M, Dutta S (2018) A novel approach for detecting and mitigating the energy theft issues in the smart metering infrastructure. Technol Econ Smart Grids Sustain Energy 3(1):13
Adikeshavamurthy S, Roopalakshmi R, Swapnalaxmi K, Apurva P, Sandhya M (2019) A novel framework for automated energy meter reading and theft detection. In: International conference on innovative computing and communications. Springer, pp 527–533
Selvapriya C (2014) Competent approach for inspecting electricity theft. Int J Innov Res Sci Eng Technol 3:1763–1766
Khoo B, Cheng Y (2011) Using RFID for anti-theft in a Chinese electrical supply company: a cost-benefit analysis. In: 2011 Wireless telecommunications symposium (WTS), New York City, NY, 2011, pp 1–6. https://doi.org/10.1109/WTS.2011.5960892
Patel K, Mishra RK (2016) A novel design to prevent electricity theft from pole mounted distribution boxes. In: 2016 National power systems conference (NPSC), Bhubaneswar, 2016, pp 1–5. https://doi.org/10.1109/NPSC.2016.7858896
Karabiber A (2019) Detecting and pricing nontechnical losses by using utility power meters in electricity distribution grids. J Electr Eng Technol 14(5):1933–1942
Commission IE et al (2002) International standard-iec 60038. IEC, Geneva
Basu K (2003) An interesting phenomenon [lighting technology]. IEEE Potentials 22(2):39–40
Abdi B, Ghasemi R, Mirtalaei S (2013) The effect of electrolytic capacitors on smps’s failure rate. Int J Mach Learn Comput 3(3):300
Oh H, Azarian MH, Das D, Pecht M (2013) A critique of the ipc-9591 standard: performance parameters for air moving devices. IEEE Trans Device Mater Reliab 13(1):146–155
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Jaiswal, S., Ballal, M.S. Fuzzy Inference Based Electricity Theft Prevention System to Restrict Direct Tapping Over Distribution Line. J. Electr. Eng. Technol. 15, 1095–1106 (2020). https://doi.org/10.1007/s42835-020-00408-7
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DOI: https://doi.org/10.1007/s42835-020-00408-7