Abstract
Stealing of electricity through meter tampering has always been a major cause not only for loss of revenue to the governments but also for irregular electricity supply. Advanced Metering Infrastructure (AMI) has replaced traditional analog devices with the digital ones like smart meters thereby enabling bidirectional flow of information between the utility and consumers via communication network. This two-way communication has the potential to flat the demand and supply curve between the utility companies and consumers. However with the adoption of new technology such as smart grid new security challenges have emerged. Although smart meters may have provided an edge over traditional methods of stealing electricity, they have opened doors for next generation of hackers. In this paper, we have provided an overview of various energy theft detection techniques in Smart Meters along with their implementation challenges with context to Indian Power Sector. A comprehensive performance comparison between different available approaches is attributed and a distinguished attack technique at the hardware level is also being proposed.
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References
Sharma T, Pandey KK, Punia DK, Rao J (2016) Of pilferers and poachers: Com-bating electricity theft in India. Energy Res Soc Sci 11:40–52
U.S. Energy Information Administration—Eia—Independent Statistics and Analysis. https://www.eia.gov/todayinenergy/detail.php?id=23452
Aggregate Technical & Commercial (AT&C) Losses in power sector. saumitra.cea@nic.in. https://data.gov.in/catalog/aggregate-technical-commercial-atc-losses-power-sector. Accessed Dec 17
Securing the smart grid of tomorrow. https://segrid.eu/
Warudkar D, Chandel P, Salwe BA (2014) Anti-tamper features in energy meters. Int J Electr, Electron Data Commun 2(5), May-2014. ISSN 2320-2084
Mesbah W (2016). Detection and correction of tampering attempts of smart electricity meters. In: PES innovative smart grid technologies conference Europe (ISGT-Europe), October, pp 1–6. IEEE
Cárdenas AA, Amin S, Schwartz G, Dong R, Sastry S (2012) A game theory model for electricity theft detection and privacy-aware control in AMI systems. In: Communication, control, and computing (Allerton), 2012 50th annual Allerton conference on, October, pp 1830–1837. IEEE
Coma-Puig B, Carmona J, Gavalda R, Alcoverro S, Martin V (2016) Fraud detection in energy consumption: a supervised approach. In: Data science and advanced analytics (DSAA), 2016 IEEE international conference on, October, pp 120–129. IEEE
Zhou Y, Chen X, Zomaya AY, Wang L, Hu S (2015) A dynamic programming algorithm for leveraging probabilistic detection of energy theft in smart home. IEEE Trans Emerg Topics Comput 3(4):502–513
Neto EAA, Coelho J (2013) Probabilistic methodology for technical and non-technical losses estimation in distribution system. Electric Power Syst Res 97:93–99
Sahoo S, Nikovski D, Muso T, Tsuru K (2015) Electricity theft detection using smart meter data. In Innovative smart grid technologies conference (ISGT), 2015 IEEE power & energy society, February, pp 1–5. IEEE
Tangsunantham N, Ngamchuen S, Nontaboot V, Thepphaeng S, Pirak C (2013) Experimental performance analysis of current bypass anti-tampering in smart energy meters. In: Telecommunication networks and applications conference (ATNAC), 2013 Australasian, November, pp 124–129. IEEE
Dineshkumar K, Ramanathan P, Ramasamy S (2015) Development of ARM processor based electricity theft control system using GSM network. In: Circuit, power and computing technologies (ICCPCT), 2015 international conference on, March, pp 1–6. IEEE
McLaughlin S, Holbert B, Zonouz S, Berthier R (2012) AMIDS: a multi-sensor energy theft detection framework for advanced metering infrastructures. In: Smart grid communications (SmartGridComm), 2012 IEEE third international conference on, November, pp 354–359. IEEE
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
Prasad J, Samikannu R (2017) Overview, issues and prevention of energy theft in smart grids and virtual power plants in Indian context. Energy Policy 110:365–374
Guerrero JI, Monedero Í, Biscarri F, Biscarri J, Millán R, León C (2014) Detection of non-technical losses: the project MIDAS. Advances in secure computing, Internet services, and applications, pp 140–164
Bula I, Hoxha V, Shala M, Hajrizi E (2016) Minimizing non-technical losses with point-to-point measurement of voltage drop between “SMART” meters. IFAC-PapersOnLine 49(29):206–211
Saha M, Thakur S, Singh A, Agarwal Y (2014) EnergyLens: combining smartphones with electricity meter for accurate activity detection and user annotation. In: Proceedings of the 5th international conference on Future energy systems, June, pp 289–300. ACM
Han W, Xiao Y (2016) FNFD: a fast scheme to detect and verify non-technical loss fraud in smart grid. In: Proceedings of the 2016 ACM international on workshop on traffic measurements for cybersecurity, May, pp 24–34. ACM
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Lehri, D., Choudhary, A. (2021). A Survey of Energy Theft Detection Approaches in Smart Meters. In: Shorif Uddin, M., Sharma, A., Agarwal, K.L., Saraswat, M. (eds) Intelligent Energy Management Technologies. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8820-4_2
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DOI: https://doi.org/10.1007/978-981-15-8820-4_2
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