Development of An Artificial Neural Network Based Time-Series Fault Predictive Model for Power System

Ganiyu Adedayo Ajenikoko, Bolarinwa Samson Adeleke


Electrical power is one of the most important forms of energy which is needed in almost every field of human endeavour. However, increase in size of electrical power structure without proper planning has negative effects on power system supplied to end users, thereby increasing the  fault level of the network. This research paper therefore, developed an Artificial Neural Network based Time Series (ANN-TS) fault predictive model for forecasting of fault levels in power system. In this paper, ANN-TS model was trained with three years (2015-2017) outage fault frequency and fault duration data obtained from Ayede 132/33 kV transmission substation of Transmission Company of Nigeria (TCN) in Ibadan using Resilient Back-Propagation (RBP) algorithm and simulation was carried out in MATLAB environment using Mean Absolute Percentage Error (MAPE) as performance metric. The model was used to predict yearly fault frequency and fault duration for twenty-three years (2018-2040). The results of the fault frequency forecast showed that monthly forecast graphs were overlapped. Yearly MAPE varied between 0.004 % and 25 %, and the feeders’ average MAPE was between 6 % and 10 %. In fault duration, the graphs followed the same pattern in nearly all the paths of the graphs, the yearly MAPE varied between 0.001 % and 25.54 % and the feeders’ average MAPE varied between 6 % and 11 %. The model produced a fairly accurate forecast according to the criteria of MAPE. The average overall MAPE of each feeder was between 6 % and 10 % which indicated between 90 % and 94 % accuracy of the model. Therefore the ANN-TS model is effective for fault prediction in reference time series.

Keywords:Electrical Power, Fault, Artificial Neural Network based Time Series, Resilient Back-Propagation, Mean Absolute Percentage Error, Forecasting, Transmission substation.

DOI: 10.7176/JETP/12-3-01

Publication date:August 31st 2022

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ISSN (Paper)2224-3232 ISSN (Online)2225-0573

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