基于数据-物理融合的直流系统后续换相失败预测方法

换相失败是高压直流输电系统最常见的故障之一,对换相失败的有效预测有利于交直流系统的安全稳定。但是,与首次换相失败相比,后续换相失败机理较为不明,影响因素更加复杂,当前研究尚难以实现对后续换相失败的有效预测。因此,文章提出了一种基于数据-物理融合模型的后续换相失败预测方法。基于对换相过程的机理分析,首先将电力系统固有响应形式进行时域-频域转换,得到考虑电压谐波的换相电压预测值。然后,基于叠加定理计算直流电流,从而实现对熄弧角的预测。为进一步提高预测精度,将与熄弧角相关的电气量作为输入特征,建立基于数据驱动的预测误差修正模型,对机理分析得到的熄弧角预测值进行校正。最后,在电磁暂态仿真软件中搭建测试系统,结果验证了文章所提方法的有效性。

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 part of physical-driven, 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 according to 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 physical-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.