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中文摘要：
输电线路故障行波频谱与故障距离之间存在数学关系，故利用故障行波频谱可以实现故障测距。直流输电线路两端平波电抗器和直流滤波器构成了直流输电系统实体物理边界，它对于行波高频部分呈近似开路特性，其反射系数接近于1，使得量测端时域波形呈周期性。对于行波低频部分，直流滤波器的频率特性使量测端的行波极性会发生翻转，致使时域波形的相角偏移，在频域上表现为自然频率的偏移。此外，故障电压行波于非对称短路点发生线模与零模行波相互交叉透射，致使故障电压自然频率“混叠”。鉴此，利用人工神经网络（artificial neural network，ANN）的非线性函数逼近拟合能力，选择故障电压自然频率的主频及其2倍频的幅值和频率作为样本属性，对神经网络进行训练、测试来确立直流输电线路故障定位的ANN模型。大量的PSCAD数字试验表明，基于自然频率和ANN的UHVDC线路故障测距方法可行、有效。
英文摘要：
The spectra of fault induced traveling wave is related to the fault distance, therefore the spectra of fault induced traveling wave can be utilized for fault location. A boundary existing in the DC system consisting of DC filter and smoothing reactor is almost the open-circuit to the high frequency signals and the reflection coefficient of the boundary is similar to one. Consequently, the wave measured at relay is a periodic signal. However, the polarity of traveling wave will be changed while hitting the DC filter which is a capacitive unit for the low frequency part of traveling wave, thus the radians of the low frequency signals and the nature frequency in frequency domain of the traveling wave will be altered. In addition, line mode and zero-mode voltage wave cross each other at the point of fault which causes the natural frequency ＂aliasing＂. Artificial neural network （ANN） that is a good tool for non-linear data modeling and non-linear function curve-fitting is selected to solve the aforementioned problems. The fundamental frequency and the 2nd harmonic are chosen as the inputs for training and testing the neural network for fault location in the UHVDC system. A variety of transmission line fault situation are simulated based on the PSCAD, and this proposed method using ANN and nature frequency shows satisfactory performance.
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