Neural network based loading margin approximation for static voltage stability in power systems

Abstract

Approximate loading margin methods have been developed using Artificial Neural Networks (NN) for static voltage stability in power systems. Artificial Neural Network is used to approximate the loading margin at particular generation direction and three different methodologies are used for finding NN training data sets. The proposed methods are validated and compared with actual loading margin and the Maximum Loading Margin methods in the modified IEEE 14-bus test system and Thailand power system. The methods will help system operators to approximate voltage stability margin or loading margin of the system in a simple way.