Sliding-Window-Based Real-Time Model Order Reduction for Stability Prediction in Smart Grid

In this paper, a new real-time model order reduction technique for stability prediction in the smart grid is proposed. The proposed method uses an online proper orthogonal decomposition algorithm. A snapshot matrix on a sliding sampling window is used for extracting the main components of the system states by performing a randomized singular value decomposition. After reducing the order of the system, a local linear model is estimated for this snapshot matrix. Then, the state of the system is predicted in a sliding prediction window. Finally, a suitable stability index is calculated and the stability of the system is forecasted in this prediction window. The proposed method is capable of predicting the transient stability, unstable/critical machines and the stability limit. In addition, it can be used for the first swing and multiswing instability detection. The simulations on three test systems show that the proposed technique can predict system stability with the high precision in real time. The computational burden and the length of prediction horizon is suitable for practical applications and the proposed algorithm has significant advantages in case of large-scale power systems.