Optimal Reconfiguration of Power System With Fuzzy ARTMAP Neural Networks

摘要

Optimal Reconfiguration of power system is one of the most important duties of control centers. It is an essential task for power system management in normal and emergency conditions. Accuracy of this procedure is essential to overcome the subsequences of static and dynamic security constraints. However, because of limited time between on line calculation procedure and implementation of the optimization output results, speed of calculation is very important. To overcome this problem, a shift from on line calculations to off line calculations can be considered and a pattern recognition procedure for online operation based on off line database may be used. It has been shown that back propagation neural networks can achieve the pattern recognition goals for different conditions, but one of the most drawbacks of the mentioned method is their slow convergence. This may reduce the effectiveness of ANN, because many ANN must be trained for different topology and load conditions of system. A neural network architecture that does not suffer from the above-mentioned drawbacks is the Fuzzy ARTMAP (Adaptive Resonance Theory-supervised predictive Mapping) neural network. In this paper, implementation of the Fuzzy ARTMAP neural network for defined problem has been proposed. In our proposed algorithm, genetic algorithm optimization for optimal reconfiguration has been used for off line stage. The performance and the efficiency of proposed method have been investigated using the IEEE 30-bus test system.