Abstract
In this work, we present a method for energy theft detection in power distribution networks—a problem in the Nigerian power system and an obstacle to national development—by network analysis. The focus was on radial systems with overhead distribution lines supported on poles. The power distribution network was modelled with typical parameters and consumer loads. In addition, a real network in Ekong Uko Street, Eket, Nigeria was surveyed and the physical structure modelled with simulated consumer and theft loads. The developed program was first initialized under conditions of no theft using the section line parameters and the actual voltage/current at each consumer node as would be reported by a smart tariff meter. The result of the initialization step is a matrix of consumer branch resistances which is stored for later use in the theft detection algorithm. Energy theft detection was achieved by comparing the actual voltages at each pole computed by propagation from all connected consumer nodes using the stored branch resistances. Differences were identified as indicators of theft and were further processed to estimate the power consumed. The result showed a dependence of detection accuracy on location of theft, relative magnitude of theft and network conditions. Minimum power theft that could be detected was between 10 W to 260 W and varied with the theft location. Accuracy in actual power consumed detection of 96% to 100% was obtained. Utility companies will find this work useful in detecting power theft in their secondary power distribution networks to arrest revenue loss.
Highlights
In this modern world, energy has become a basic necessity for national development; its availability in the required quantity enables reduced work hours, better agricultural yield, optimized industrial production, superior health conditions, more reliable transportation infrastructure/machines and even more nourishing diets [1,2]
A study on a typical Nigerian power station [6] showed that non-technical losses far exceeded the technical losses; Port Harcourt Electricity Distribution Company in Nigeria reported a monthly loss of N233 million [13]
Electric energy theft is a global problem, combating it should be a critical task especially for a developing country like Nigeria that generates less than 30% of the national demand [35] and yet more than 50% of the paltry sum is lost
Summary
Energy has become a basic necessity for national development; its availability in the required quantity enables reduced work hours, better agricultural yield, optimized industrial production, superior health conditions, more reliable transportation infrastructure/machines and even more nourishing diets [1,2]. Generators/transformers have losses due to their winding resistance, hysteresis and eddy current; transmission lines have corona losses at higher voltages; distribution lines have thermal (I2R) losses; cables have dielectric losses and even tariff meters have associated losses [8] These losses due to the non-ideal nature of the electrical equipment are known as technical losses; they can be estimated from the load flow study and minimized through the use of more efficient equipment/designs [9]. The statistical data analysis approach proposed in [20] is based on the premise in [21] that electricity bills follow Benford’s law Combining this with a Stackelberg game model, the authors analyzed the consumption data logged from smart meters to detect compromised meters. The rest of this paper is organized as follows: in Section 2, we describe the network structure, model and parameters used for network analysis; Section 3 contains the theft detection model including the five unique cases that were analyzed; the results from a case study network is presented in Section 4 while conclusions and references make up Sections 5 and 6 respectively
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