OPTIMAL POWER SYSTEM CONTROL USING ARTIFICIAL NEURAL NETWORKS

Abstract

Electric power system is a highly complex and non linear system. Its analysis and control in real time environment requires highly sophisticated computational skills. Computations are reaching a limit as far as conventional computer based algorithms are concerned. It is therefore required to find out newer methods which can be easily implemented on dedicated hardware. It is a very difficult task due to complexity of the power system with all its interdependent variables, thus making the neural networks one of the better options for the solution of different issues in operation and control. In this project an attempt has been made to implement ANN’s for observability determination, State Estimation, Economic Load Dispatch and for Reactive Power Optimization. A Hopfield neural network model has been developed to test Topological Observability of Power System and it is tested on two different test systems. The results so obtained, are comparable with those results of conventional root based observability determination technique. Further a Hopfield model has been developed to determine State Estimation of power system. State Estimation of 6 bus system and IEEE 14 bus system is attempted using this Hopfield neural network. Results obtained by developed model are compared with those of conventional Non Linear WLS State Estimation. Next use of ANN for Economic Load Dispatch problem has been developed. Economic Load dispatch has been studied using various test system data (like 3, 6, 20 & 30 units) and the results are compared with conventional Lambda iterative technique and Particle Swarm Optimization techniques. Next Reactive Power Optimization problem has been attempted using ANN. The performance of so developed ANN is tested on Ward Hale 6 bus system and IEEE 30 bus system data and the results obtained are compared with those of the results obtained by GA and Particle Swarm Optimization technique.

Keywords: ANN, Load dispatch, optimization

DOI: 10.7176/ISDE/10-6-01

Publication date:July 31st 2019