Prediction of Electromechanical Oscillatory Parameters in Power Systems Using ANN

The increase in electric power demand pushes the modern power system for more interconnected networks. It leads to a lack of inertia and creates more critical disturbances in the power system. It may not be adequately damped out and results in cascade tripping. Immediate detection of low-frequency oscillatory modes and their parameters will help the power system operator to act on a particular event without consuming much time. This paper proposes a dynamic method for the oscillatory mode parameter estimation in a power system using an Artificial Neural Network (ANN). An ANN model is created to analyze the power oscillation disturbance within the system, and it is trained using the Hilbert transform method to estimate the instantaneous parameters. Once the ANN model is trained for different power disturbance situations, it can be used for any events associated with the system. Simulation results are verified using two area Kundur system at different disturbance conditions.