针对集合经验模态分解（EEMD）用于双馈风电场送出线路行波故障定位中行波检测精度不高，存在模态混叠、抗噪能力弱及故障定位实时性不好等问题，提出了一种基于快速集合经验模态分解（FEEMD）与改进Teager能量算子（NTEO）结合的行波故障定位方法。该方法利用FEEMD对故障电流行波信号进行分解，分解为平稳的固有模态分量和残差分量，消除噪声成分，保留信号的完整性；然后采用NTEO算法对分解的高频信号再次去噪，增强故障行波突变特征，精确标定行波波头。仿真结果表明，所提方法能够快速将故障行波波头精确标定，且去噪效果好，与FEEMD-TEO、EEMD-NTEO行波检测方法相比，提高了故障定位的精度和速度。

Aiming at the problems of low traveling wave detection accuracy, mode mixing, weak anti-noise ability, and poor real-time fault location in the use of collective empirical mode decomposition (EEMD) for traveling wave faults localization in doubly-fed wind farm transmission lines, a traveling wave fault localization method based on the combination of fast ensemble empirical modal decomposition (FEEMD) and the improved Teager energy operator (NTEO) is proposed. This method uses FEEMD to decompose the fault current traveling wave signal into stationary intrinsic mode components and residual components, eliminating the noise components and preserving the signal integrity. Then the NTEO algorithm is used to denoise the decomposed high-frequency signal again to enhance the faulty traveling wave mutation characteristics and and accurately calibrate the traveling wave head. Simulation results show that the proposed method can accurately and quickly calibrate the fault traveling wave head precisely with good denoising effect, which improves the accuracy and speed of fault location compared with FEEMD-TEO and EEMD-NTEO traveling wave detection methods.

国家自然科学基金项目(61271159)；云南省地方高校联合基金重点项目(202101BA070001039)；云南省教育厅科学研究基金项目资助(2023J1057)；校级大学生科研项目(XSKY2314)