基于深度学习的特高压三端混合直流输电线路波形特征故障区域判别方法

Fault Zone Identification Method for Three-terminal Hybrid UHVDC Transmission Lines Based on Deep Learning and Waveform Characteristics

针对将现有直流线路故障区域识别方法应用于特高压三端混合直流输电线路时,存在难以区分T区两侧故障、耐过渡电阻能力弱和阈值整定困难的问题,提出一种利用深度学习及波形特征进行特高压三端混合直流输电线路故障区域识别的方法。首先,对三端混合直流线路不同故障区域进行故障特征分析;然后,对线模电压和线模电流进行多尺度小波分解,提取线模电流中低频分量和线模电压高频分量,结合正负极电压波形特征,组成深度学习模型的输入量,并将故障区域作为输出量,构建深度学习故障区域识别模型;最后,用训练过的深度学习模型对获取的故障特征量进行处理,以实现故障区域识别的目的。通过大量仿真实验,验证了所提故障区域识别方法具有准确率高和基本不受过渡电阻影响的特性。

When the existing DC line fault zone identification methods are applied to three-terminal hybrid UHVDC transmission lines,there exist problems such as difficulty in distinguishing faults on both sides of the T-zone,weak ca⁃pability to endure the transition resistance,and difficulty in threshold setting.Aimed at these problems,a method is proposed to identify the fault zones of three-terminal hybrid UHVDC transmission lines by using deep learning and wave⁃form characteristics.First,the fault characterization of different fault zones of three-terminal hybrid DC lines is carried out.Second,a multi-scale wavelet decomposition of line mode voltage and line mode current is carried out to extract the low-and medium-frequency components of line mode current and high-frequency components of line mode voltage.These components form the input to the deep learning model by combining the waveform characteristics of positive and negative voltage,while the fault zone is taken as the output,the deep learning faulty region recognition model is con⁃structed.Third,the acquired fault characteristics are processed by the trained deep learning model to achieve the fault zone identification.Through a lot of simulations,it is verified that the proposed fault zone identification method has a high accuracy and is basically unaffected by the transition resistance.