Fault prediction model on electrical power network using artificial neural network-based time series: A case study of Ayede-Eruwa/ Lanlate Feeder
1 Department of Electrical Engineering, Adeseun Ogundoyin Polytechnic, Eruwa, Nigeria.
2 Department of Science Laboratory Technology, Adeseun Ogundoyin Polytechnic, Eruwa, Nigeria.
3 Department of Electrical and Electronic Engineering, University of Ibadan, Ibadan, Nigeria.
4 Department of Mathematics and Statistics, Adeseun Ogundoyin Polytechnic, Eruwa, Nigeria.
Research Article
International Journal of Frontline Research in Engineering and Technology, 2022, 01(01), 001–016.
Article DOI: 10.56355/ijfret.2022.1.1.0004
Publication history:
Received on 09 February 2022; revised on 29 March 20222; accepted on 31 March 2022
Abstract:
Increase in size of electrical power network usually results in a rise in fault level and consequently in huge economic losses to energy providers and consumers in the distribution systems. Therefore, it is very important to be proactive in dealing with faults on distribution feeder systems not only to reduce financial havoc, but to save lives and improve the quality of life of the people. A case study of Ayede-Eruwa/Lanlate Oyo State, Nigeria 33kV line is considered. An Artificial Neural Network based Time Series (ANN-TS) fault predictive model is developed for forecasting of faults on the above chosen electrical power network. Daily forced outage readings of the substation’s feeders for three years were collected and modeled using a three-layer feed-forward network ANN-TS. The results in the frequency of fault prediction show that there is an overlap between the observed and predicted values. The annual Mean Average Percentage Error (MAPE) varies between 0.004% and 25%, and the feeders’ average MAPE ranges from 6% to 10%. The fault duration annual MAPE varies between 0.001% and 25.54% while the feeders’ average MAPE varies between 6% and 11%. The energy loss prediction follows the same trend with the annual MAPE alternating between 0.01% and 26.75%, andthe feeders’ average MAPE between 6% and 10%. The average overall MAPE of each feeder is between 6% and 10% which indicates that the developed model is about 90% to 94% accurate. Although, the model is designed for Ayede-Eruwa/Lanlate feeder, it could be utilized for effective prediction of faults in any power distribution network.
Keywords:
Fault prediction; Artificial neural network (ANN); Feeder; MAPE; Distribution
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