@article{10.11648/j.eas.20200502.12,
  author = {Olusegun Mayowa Komolafe and Kingsley Monday Udofia},
  title = {A Technique for Electrical Energy Theft Detection and Location in Low Voltage Power Distribution Systems},
  journal = {Engineering and Applied Sciences},
  volume = {5},
  number = {2},
  pages = {41-49},
  doi = {10.11648/j.eas.20200502.12},
  url = {https://doi.org/10.11648/j.eas.20200502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.eas.20200502.12},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - A Technique for Electrical Energy Theft Detection and Location in Low Voltage Power Distribution Systems
AU  - Olusegun Mayowa Komolafe
AU  - Kingsley Monday Udofia
Y1  - 2020/04/17
PY  - 2020
N1  - https://doi.org/10.11648/j.eas.20200502.12
DO  - 10.11648/j.eas.20200502.12
T2  - Engineering and Applied Sciences
JF  - Engineering and Applied Sciences
JO  - Engineering and Applied Sciences
SP  - 41
EP  - 49
PB  - Science Publishing Group
SN  - 2575-1468
UR  - https://doi.org/10.11648/j.eas.20200502.12
AB  - 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.
VL  - 5
IS  - 2
ER  - 

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