针对行波波头标定的精度不足以及智能定位模型拟合性能易受参数影响的问题，提出了一种基于白鲸算法优化变分模态分解和核极限学习机的柔性直流输电线路短路故障定位方法。首先，采用白鲸算法优化变分模态分解的参数，结合小波软阈值去噪方法对采集的故障信号进行降噪和分解，再结合希尔伯特变换标定初始行波的到达时刻。其次，将行波的到达时刻作为特征值构建特征数据集，用白鲸算法优化核极限学习机定位模型。最后，将数据集代入到优化后的定位模型中实现故障定位。结果表明，该方法的定位模型拟合程度达到99.4%，具有较高的定位精度和较好的鲁棒性，所提方法对噪声和过渡电阻的耐受性能较高，定位误差在500 m以内。

Aiming at the lack of accuracy of traveling wave head calibration and the performance of intelligent location model affected by parameters, a short-circuit fault location method based on the beluga whale algorithm (BWO) is proposed to optimize variable mode decomposition (VMD) and kernel extreme learning machine (KELM) for MMC-HVDC transmission lines. Firstly, the BWO is used to optimize the parameters of VMD， combined with wavelet soft threshold denoising method for noise reduction and decomposition of the collected fault signals. Then the arrival moment of the initial traveling wave is calibrated by combining the Hilbert transform (HT). Next, the arrival moments of traveling waves are used as eigenvalues to construct the feature dataset. The KELM localization model is optimized using BWO. Finally, the dataset is substituted into the optimized localization model to achieve fault localization. The results show that the localization model of the method fits 99.4% with high localization accuracy and good robustness. The proposed method is highly tolerant to noise and transition resistance, and the localization error is within 500m.