Transient Stability Assessment Using ANN Considering Power System Topology Changes

Abstract

This paper presents an application of Artificial Neural Network (ANN) for monitoring transient stability assessment (TSA) considering system topology changes. Offline trained ANN makes use of the advance of online TSA in specifying the proper remedial action for real time power system operation to counteract the system instability. The training of ANN is executed using important selected features as inputs and critical fault clearing time (CCT) at pre-selected set of critical contingencies as desired target. CCT is considered as an indicator for system transient stability criterion. Multilayer feed forward neural network trained with back-propagation algorithm is used to provide the CCT. To demonstrate the effectiveness of the ANN in estimation of TSA considering various system topologies, the method is verified on 66-bus power system and the results are compared with time domain simulation (TDS). The simulation results indicate that the ANN provides a fast and accurate tool to evaluate online power system transient stability with acceptable accuracy.