Dynamic Mode Decomposition for Prediction and Enhancement of Rotor Angle Stability

In the era of data-driven, the next state of the system can be predicted and controlled with the help of system data set even in the absence of system model knowledge. Dynamic Mode Decomposition (DMD) algorithm is one of the techniques for predicting system states by breaking data into its principal modes, derived from a compilation of training data. The principal modes are useful for finding the behavior of the system and for predicting its future states, even in a noisy environment. The paper focuses on predicting variation in the rotor angle during a severe fault on the power system with the help of DMD. The paper also proposes the enhancement in the rotor angle with the help of Model Predictive Control (MPC) technique. For prediction and enhancement of the rotor angle, the multi-machine and single-machine systems are considered in the paper and the results are obtained for various operating scenarios. The results show the effectiveness of the proposed technique for prediction and enhancement of the rotor angle in the case of multi-machine and single-machine system with the help of system data only.