Reliability Prediction in the Nigerian Power Industry Using Neural Network
| Autor: | AIROBOMAN Abel Ehimen, IDIAGI Neville Simon |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Journal of Electrical and Electronics Engineering, Vol 13, Iss 2, Pp 17-22 (2020) |
| Druh dokumentu: | article |
| ISSN: | 1844-6035 2067-2128 97645796 |
| Popis: | In this paper, the reliability forecasting of feeders in the Nigerian Power Industry (NPI) using Artificial Neural Network (ANN) was presented. Five years (2011 – 2015) historical monthly reliability data for Guinness, GRA, Koko, Ikpoba-Dam, Etete, Nekpenekpen and Switchstation feeeders from the Benin Utility Transmission Company in Edo State were collected from literature. The ANN model was developed for each feeder in the network and was trained using the Back Propagation Forward Feed (BPFF) supervised learning approach and a projection was done till the year 2025. A minimum and maximum error of 0.0092 and 0.04 obtained from the network validated the use of ANN for this study. Further results show that the reliability of the feeders would reduce slightly by: 1.07%, 1.32%, 1.36%, 2.99%, 3.97% for Nekpenekpen, GRA, Ikpobadam, Switchstation, Etete, and significantly by: 11.97% and 12.06% for Guinness and Koko feeders respectively. The results from this study therefore, has shown that the reliabillity of the feeders in the NPI would drop in the near future and hence requires urgent measures to mitigate this challenge. |
| Databáze: | Directory of Open Access Journals |
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