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All Journal International Journal of Electrical and Computer Engineering Aceh Journal of Animal Science
Isaac, Samuel
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Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Immunology & microbiology Physics Veterinary

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Journal : International Journal of Electrical and Computer Engineering

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Voltage collapse prediction using artificial neural network Isaac, Samuel; Adebola, Soyemi; Ayokunle, Awelewa; James, Katende; Claudius, Awosope
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp124-132

Abstract

Unalleviated voltage instability frequently results in voltage collapse; which is a cause of concern in power system networks across the globe but particularly in developing countries. This study proposed an online voltage collapse prediction model through the application of a machine learning technique and a voltage stability index called the new line stability index (NLSI_1). The approach proposed is based on a multilayer feed-forward neural network whose inputs are the variables of the NLSI_1. The efficacy of the method was validated using the testing on the IEEE 14-bus system and the Nigeria 330-kV, 28-bus National Grid (NNG). The results of the simulations indicate that the proposed approach accurately predicted the voltage stability index with an R-value of 0.9975 with a mean square error (MSE) of 2.182415x10−5 for the IEEE 14-bus system and an R-value of 0.9989 with an MSE of 1.2527x10−7 for the NNG 28 bus system. The results presented in this paper agree with those found in the literature.
Co-Authors Adebola, Soyemi Adeoye, Samson O. B. Akintunde, Afolabi Rotimi Ayokunle, Awelewa Claudius, Awosope Istifanus, Emmanuel Filian James, Katende Oguntoye, Mutiu Ayogbe Olusiyi, Japhet Adeniyi