Adaptive scheme for local prediction of post-contingency power system frequency

Abstract

The power system frequency always should be kept upper than a minimum threshold determined by the limitations of system equipments such as synchronous generators. In this paper a new method is proposed for local prediction of maximum post-contingency deviation of power system frequency using Artificial Neural Network (ANN) and Support Vector Regression (SVR) learning machines. Due to change of network oscillation modes under different contingencies, the proposed predictors adjust the data sampling time for improving the performance. For ANN and SVR training, a comprehensive list of scenarios is created considering all credible disturbances. The performance of the proposed algorithm is simulated and verified over a dynamic test system.