Power System Steady State Monitoring Using Artificial Neural Network

This article presents the application of Artificial Neural Network (ANN) for steady state monitoring of a power system. In steady state monitoring of a power system, it is important to predict the line flows and bus voltages for different operating conditions. In this article ANN has been proposed as an alternative method to solve the power system problems where the desired speed has not been achieved by conventional methods. The proposed method describes an adoptive pattern recognition approach based on highly parallel information processing. We provide a pattern of system description parameters to a neural network and net returns an estimate of line flows and bus voltages. Training data were obtained by Newton Rapson load flow simulation using Mipower Software Simulation Package for different system topologies over a range of load levels and the results were compiled to form the training data set. A back propagation algorithm was used for training ANN. Results of this approach help the power system operator to successfully handle the topologically independent steady state security assessment. To illustrate the proposed approach, IEEE-14 Bus system was considered. The difference between the actual and the estimated power flows and bus voltages was found to be good in terms of accuracy.

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