Accurate and reliable fault location technology is essential to the stable operation of the modular multilevel converter‐high voltage direct current (MMC‐HVDC) system. Aiming at the difficulty of locating high‐resistance ground faults on MMC‐HVDC transmission lines, this paper proposes a method for fault location of transmission lines based on wavelet transform and deep belief network (DBN). First, the wavelet transform is used to decompose the original single pole ground fault voltage waveform, and then the high‐frequency and low‐frequency components obtained are used as training samples to train different DBN models, the final fault location results are obtained by superimposing the outputs of each model at last. The ±250 kV double‐ended MMC‐HVDC system model is established by using PSCAD/EMTDC, which can simulate the faults of different positions and different transition resistances. In order to verify the fault location performance of the proposed method, it is compared with two machine learning fault location methods. The results show that this method can accurately and reliably locate the single pole‐to‐ground fault of the transmission line with transition resistance of up to 10 000 Ω at low sampling frequency of 20 kHz. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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基于小波分解和DBN的MMC-HVDC系统单极接地故障定位方法

准确可靠的故障定位技术对于模块化多电平转换器-高压直流（MMC-HVDC）系统的稳定运行至关重要。针对MMC-HVDC输电线路高电阻接地故障的定位难题，提出了一种基于小波变换和深度信念网络（DBN）的输电线路故障定位方法。首先，利用小波变换分解原始的单极接地故障电压波形，然后将获得的高频分量和低频分量用作训练样本，以训练不同的DBN模型，通过叠加获得最终的故障定位结果最后每个模型的输出。使用PSCAD / EMTDC建立了±250 kV双端MMC-HVDC系统模型，可以模拟不同位置和不同过渡电阻的故障。为了验证所提出方法的故障定位性能，将其与两种机器学习故障定位方法进行了比较。结果表明，该方法可以在20 kHz的低采样频率下准确可靠地定位传输线的单极接地故障，其过渡电阻高达10000Ω。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。结果表明，该方法可以在20 kHz的低采样频率下准确可靠地定位传输线的单极接地故障，其过渡电阻高达10000Ω。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。结果表明，该方法可以在20 kHz的低采样频率下准确可靠地定位传输线的单极接地故障，其过渡电阻高达10000Ω。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。