Artificial Neural Network Approach of Fault Detection and Identification in 330kV Onitsha-New Haven three Phase Transmission Line

Research Area: Volume 10,Issue 3,May, 2021 Year: 2021
Type of Publication: Article Keywords: Power Line, Neural Network, Fault Detection, Training Data, Three Phase Fault
  • Ahmed Rufai Salihu
Journal: IJEIR Volume: 10
Number: 3 Pages: 110-122
Month: May
ISSN: 2277-5668
This work focuses on the presentation of detection and identification of faults on power transmission line by way of artificial neural network. Power lines are usually liable to faults. It is always expedient to quickly and accurately detect and establish the faults for clearance. ANN identifies faults in large power systems easier than the conventional methods. Onitsha-New haven 330kV Nigerian network was used in this study. The network was modeled and simulated variously in MATLAB environment. The neural network was trained using the values of phase voltages and currents as inputs. The values were scaled with respect to pre-fault values. Analysis of the performance and test of the neural network were carried out. Various possible kinds of faults were examined. Results were gotten for ten (10) fault conditions. The faults were correctly detected and identified. These results show that artificial neutral network is efficient in detection and recognition of power transmission lines’ faults.

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