Preventive Control Stability Via Neural Network Sensitivity

Abstract

This paper discusses the power systems stability margin improvement by means of preventive control based on generation re-dispatch using a neural sensitivity model. This model uses multilayer perceptron networks with memory structure in the input layer. The training of this model is made with temporal data samples from time domain simulations, incorporating information about the dynamic behavior of the system, unlike the methods proposed in the literature in which the pre-fault system data are used instead. The sensitivity is used as a guideline in selecting the most effective set of generators in the reallocation of the amount of active power capable of increasing system security. The effectiveness of the proposed methodology has been demonstrated through the application to a large system.