A comparison between ANN based methods of critical clearing time estimation

This paper presents a methodology based on Artificial Neural Network (ANN) structures for the dynamic security assessment (DSA) of power systems. Proposed methodology involves, ANN approach for fast and accurate estimation of critical clearing time (CCT) values of credible faults occurring in the system, considering changes in the loading conditions and system topology. CCT is an important indicator that measures the transient stability of the system against critical contingencies. Offline trained ANNs can monitor online DSA without suffering from excessive computational burden of time domain simulations (TDS). Decision Trees are used as a feature selection tool to reduce the training time and ANN complexity, increasing the CCT estimation performance of the ANN applications studied in this work, Multi-Layer Perceptron, Radial Basis Neural Network, Generalized Regression Neural Network and Adaptive Neuro-Fuzzy Inference Systems. The proposed approach is applied to 16 generator-68 bus test system operating at various loading conditions and system topologies.