Voltage collapse prediction using artificial neural network
| Main Authors: | Samuel Isaac, Soyemi Adebola, Awelewa Ayokunle, Katende James, Awosope Claudius |
|---|---|
| Format: | Article Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4629415 |
Daftar Isi:
- 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.