Real-Time Monitoring of Post-Fault Scenario for Determining Generator Coherency and Transient Stability Through ANN

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Abstract
Power system monitoring and control in real-time is a challenging task for modern power system due to large operational constraints. The deployment of Phasor Measurement Units (PMUs) at key locations provides an opportunity for devising effective power system monitoring and control measures. In this study, a new method is proposed to determine the real-time transient stability status and identification of the coherent generator groups by predicting the rotor angle values following a large disturbance through Radial Basis Function Neural Network (RBFNN). The first six cycle of synchronously sampled post-fault data measurements from PMUs consisting of rotor angles and voltages of generators are taken as the input to the neural network to predict the future state of the system. The proposed method can also determine the synchronism state of the individual machine in real-time. The proposed scheme is demonstrated on IEEE-39 bus test system at different operating conditions

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