Abstract
In this paper, power system stability conditions driven by voltage instability and line congestion are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. The proposed methodology has been tested on the standard IEEE 30-bus test system with the support of MATLAB based neural network toolbox. The results presented in this paper signify that the multi-layered feed forward neural network with Levenberg-Marquardt backpropagation algorithm gives the best training performance of all possible cases considered in this paper, thus validating the proposed methodology.
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