An improved decision tree-based method for predicting overvoltage peak values integrating a model-driven scheme

The commutation failure is the most prevalent fault in line-commutated converter based HVDC systems, which may result in transient overvoltage on the sending-side system. Overvoltage level evaluation has become a crucial task for power industries to assess the tripping risk of large-scale wind turbines and implement effective stability control measures. In this paper, decision tree (DT) model is adopted to extract the mapping relationship between transient overvoltage and massive electrical quantities of power grids. The common DT algorithm is transformed by modifying the error weight assignment, which reflects the error tolerances for different actual overvoltage regions. To compensate for potential inaccuracies in the data-driven method, a derivation of the mathematical relationship between the reactive power consumed by the rectifier and AC voltage is presented, along with an analytical expression for the peak value of transient overvoltage. On this basis, an overvoltage analysis method integrating the model-driven and data-driven techniques is proposed, and the improved DT algorithm is ap-plied to fast error correction, enhancing the interpretability of regression prediction results. Case studies were performed in the actual Northwest China local region hybrid AC/DC power grid with transient overvoltage problems, and the simulation results verified the effectiveness of the proposed method.