Subsequent Commutation Failure Prediction of HVDC by Integrating Physical-driven and Model-driven Methods

Commutation failure (CF) is one of the most common faults in traditional HVDC system. Effective prediction of CF is beneficial to the safety and stability of the power system. The physical-driven prediction method can effectively reflect the causal law but it is difficult to establish a precise model. Data-driven prediction method has the advantage of efficient training, but the prediction accuracy depends on a large number of high-quality training samples. Combining the advantage of physical-driven and data-driven methods, a CF prediction method is proposed. In the physical-driven part, the inherent response of the power system is transformed from time-domain to frequency-domain to obtain the predicted commutation voltage. Then the predicted DC current can be obtained based on the superposition theorem. Finally, the predicted extinction angle can be calculated according to the commutation mechanism. In the part of data-driven, the amplitude and phase of each harmonic of the commutation voltage are taken as the input characteristics, and the extinction angle predicted by the physi-cal-driven method can be modified. According to the results of the test system built in electromagnetic transient simulation software, the validation of the proposed method is verified.