Sliding-Window-Based Real-Time Model Order Reduction for Stability prediction in smart Grid


A new real-time model order reduction technique for smart grid stability prediction is proposed in this work. An online appropriate orthogonal decomposition algorithm is used in the suggested method. A randomised singular value decomposition is used to extract the major components of the system states using a snapshot matrix on a sliding sample window. For this snapshot matrix, a local linear model is estimated after the system's order is reduced. The system's state is then forecasted using a sliding prediction window. Finally, a suitable stability index is determined, and the system's stability is predicted within this prediction window. Transient stability, unstable/critical machines, and the stability limit may all be predicted using the suggested method. It can also be used to detect first-swing and multiple-swing instability. The suggested technique can forecast system stability with high precision in real time, according to simulations on three test systems. The computational cost and forecast horizon length are appropriate for realistic applications, and the proposed approach offers significant benefits in the case of large-scale Power Systems.

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PROJECT TITLE : Sliding-Window-Based Real-Time Model Order Reduction for Stability Prediction in Smart Grid ABSTRACT: In this paper, a new real-time model order reduction method for smart grid stability prediction is presented.
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