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Online Cascading Failure Searching Based On Gradient Boosting Decision Tree
With the penetration of renewable energy sources (RES) increasing rapidly, cascading failures become more complex due to the uncertainty and vulnerability of RES. This paper presents an online cascading failure screening method based on gradient boosting decision tree (GBDT) for hybrid AC/DC system with high penetration of wind power. First, the cascading failure risk index is established, which considering the impact, probability, and loss of failures. Then, a cascading failure screening based on Monte Carlo tree search and dynamic simulation is performed to acquire the high-risk cascading failures, which are utilized as the training samples. Finally, GBDT is deployed to construct the relationship between operating conditions and failure propagation directions. Online cascading failure screening is realized by combining GBDT failure estimation and dynamic failure simulation. Simulation results of the modified New England 39-bus test system demonstrate that the proposed method can fast and accurately online screening the high-risk cascading failures considering the uncertainty of wind power.