Distribution line monitoring system for the detection of power theft using power line communication

Christopher, Alwin Vinifred; Swaminathan, Guhesh; Subramanian, M.; Thangaraj, P.

doi:10.1109/cencon.2014.6967476

Cited by 21 publications

(9 citation statements)

References 5 publications

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“…The following is a work of several researchers' on theft detection models: In [23] study employs a method based on the power line communication concept, which is used to identify electricity theft. A high-frequency signal is introduced to the distribution network that fluctuates in amplitude and frequency as the load on the lines rises or falls.…”

Section: Related Workmentioning

confidence: 99%

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

et al. 2021

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The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

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Section: Related Workmentioning

confidence: 99%

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

et al. 2021

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“…Also, equations ( 17) and ( 18) depict the percentage of range bounds in equations ( 15) and ( 16) for an altered user meter respectively. While for accurate range bounds in relation to (17) and (18), unaltered user meter λt is set to zero.…”

Section: Power Theft Analysis In the Absence Of Meter By-passingmentioning

confidence: 99%

Design and Hybrid Simulation of a Larceny Deterrent Energy Evaluation System

Enobakhare

Ogunbor

2022

Int. J. Emerg. Sci Res

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The unreliability of the energy system to provide a proper account of energy utilized by consumers has been a huge burden on the distribution system network. Different metering methods and designs to detect and prevent fraud, employed in the past have proven fruitless, thus signalling the need for a much smarter energy metering system. The most frequent problem is electricity larceny, this has incurred a major economic loss in the energy distribution system. To this end, this paper presents the distinctive design and hybrid simulation of a larceny deterrent energy evaluation system, capable of detecting different methods of energy theft within power consumer premises. The method employed comprises of deep understudy of previous work in this field, a model is proposed and is simulated under good working conditions and several theft situations using MATLAB while the hardware is simulated using Proteus 8.1 and Arduino software. In conclusion, the efficiency of the proposed system is evaluated by employing different electric theft algorithms, with the results indicating significant energy cost savings in the distribution network.

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“…By providing proper isolation from the power frequency signal, the system can be implemented without any source for damage to the oscillator and the entire detection circuit [56].…”

Section: Cybercriminals Switched Towards Different Services Such Asmentioning

confidence: 99%

Methods of identifying unauthorized customers in cryptocurrency mining of distribution networks-a review

Najafzadeh

Gholami

2022

2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS)

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Distribution line monitoring system for the detection of power theft using power line communication

Cited by 21 publications

References 5 publications

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Design and Hybrid Simulation of a Larceny Deterrent Energy Evaluation System

Methods of identifying unauthorized customers in cryptocurrency mining of distribution networks-a review

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