Estimation of critical clearing times using neural networks

The conventional transient stability measure of power system robustness to withstand large disturbances is usually named critical clearing time (CCT). The CCT evaluation involves elaborate computations that often include time-consuming solutions of nonlinear on-fault system equations. Among several approaches that have been proposed in the literature to meet very stringent needs of transient stability analysis, recent works suggest that artificial neural networks (ANNs) may be particularly appropriate. In this work, we propose the use of a radial basis function network (RBFN) for estimating the CCT of single faults in power systems. The proposed method has been applied for online transient stability analysis of a small power system (45 buses, 72 lines and/or transformers, and 10 generators). For RBFNs training purposes, we used 19 contingencies with 800 stability scenarios each. For verifying the RBFNs global performance, it was analysed the RBFN sensitivity with respect to the neuron numbers in hidden layer (25, 50, 75, and 100 neurons) for a set of 100 test cases. The numerical results provide a very good global performance index (mean relative error lesser than 3.65%).