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


In this paper, a new real-time model order reduction method for smart grid stability prediction is presented. One of the proposed algorithms is based on an online proper orthogonal decomposition (POD). Singular value decomposition is used to extract the major components of the system states from a sliding sampling window using a snapshot matrix. For this snapshot matrix, a local linear model is estimated after the system's order is reduced. Then, a sliding prediction window is used to forecast the future state of the system. When the system is finally stabilised, a stable index is calculated, and the system's stability is projected in this prediction window. Transient stability, critical machines, and stability limits can all be predicted using this method. The initial swing and multiswing instability detection can also be employed with it. System stability can be predicted with great precision in real time using simulations on three test systems. Using the suggested algorithm for large-scale Power Systems offers considerable advantages because of the computing overhead and the length of the prediction horizon.

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