Samuel, I. A. and Soyemi, Adebola and Awelewa, A. A. and Katende, J. and Awosope, C. O. A. (2021) Voltage collapse prediction using artificial neural network. International Journal of Electrical and Computer Engineering (IJECE), 11 (1). pp. 124-12. ISSN 2088-8708
![[img]](http://eprints.covenantuniversity.edu.ng/style/images/fileicons/application_pdf.png) |
PDF
Download (533kB) |
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Online voltage stability analysis Voltage stability Voltage stability index Weakest bus |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Mrs Patricia Nwokealisi |
| Date Deposited: | 22 Jun 2021 19:39 |
| Last Modified: | 22 Jun 2021 19:39 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/14856 |
Actions (login required)
 |
View Item |
