Fast Prediction of Cascading Commutation Failure in Multi-infeed HVDC System by Integrating Data-driven Method and Model-driven Method

In this paper, a prediction model of cascading commutation failure (CF) based on integration of data-driven method and model-driven method is proposed. The impact of AC system fault is quantified by a data-driven model, which is trained by multiple simulation cases with different fault locations and durations. Extreme learning machine (ELM) is applied in this data model for its fast calculation. Adjusted multi-infeed interaction factor (MIIF) is firstly proposed that considers nonlinearity of voltage interactions between converter stations. Then, voltage sensitivity matrix among converter stations is calculated based on adjusted MIIF, which is a simplified physics model established to compute the voltage drop in target station due to the voltage drop at neighbor stations. Afterwards, the total voltage drop on target station is obtained by adding the output of data model and physics model. Finally, the prediction of CF is given by comparing the predicted voltage with critical voltage. The method is verified in a two-infeed HVDC test system and East-China power grid. Extensive simulations are conducted in PSD-BPA and the result shows the advantage in speed and accuracy of the proposed model.