A real time approach to identify actions to prevent voltage collapse using genetic algorithms and neural networks

In this paper we describe a new approach to identify the combination of tap transformer positions, capacitor bank steps together with the minimum amount of load to be shed that assures one to obtain a specified security degree of a power system. The basic approach is designed to identify the most adequate actions to be taken for a given contingency. This identification procedure uses genetic algorithms given their adequacy to model discrete actions. However, genetic algorithms are known for their usually large computation time. In order to address this issue and having in mind the objective of developing a real time tool, we incorporated a classification procedure based on neural networks. The paper includes results obtained using the developed approach both to evaluate the quality of the solutions for a number of contingencies and the quality of the overall performance when using the neural network tool. Results obtained for a reduced version of the Brazilian Mate Grosso transmission system are presented and discussed