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.
Collaboration network of the authors
13 authors ·
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· 15 across different organisations
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Covenant University
Tshwane University of Technology
Namibia University of Science and Technology
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