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中文摘要：
固有频率与故障距离之间存在数学关系,故障行波暂态能量在固有频率附近较集中,其暂态能量包含丰富的故障距离信息.利用人工神经网络（ANN）的非线性函数逼近拟合能力,建立直流输电线路故障定位的ANN模型.利用小波变换的等距特性提取单端线模电压7尺度的小波能量,并将其作为样本属性对神经网络进行训练、测试.所提方法将不易提取的固有频率点特征转化为容易提取的频带特征,提高了测距的可靠性.数字实验结果表明,所提方法在不同过渡电阻和不同故障距离下均能准确测距.
英文摘要：
The inherent frequency of fault traveling wave is mathematically associated with fault distance and its transient energy containing rich information about fault distance is concentrated around this frequency.Because of its fitting capability for non-linear function,an ANN（Artificial Neural Network） model of HVDC line is built to locate its faults.Based on the equidistant characteristic of wavelet transform,the transient energy spectrum of line voltage modulus at one end is extracted in seven scales,which are used as the samples to train and test the ANN model.The proposed method takes the inherent frequency band,instead of point,to extract fault information,which is easier and more reliable.Results of digital test show faults at any line position and with any transition resistance can be accurately located.
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