基于小波能量谱和SSA-GRU的混合直流输电系统故障测距方法

针对混合直流输电系统故障测距存在行波波头难以识别以及固有主频不易提取的问题，提出一种基于小波能量谱和麻雀搜索算法(sparrow search algorithm, SSA)优化的门控循环单元(gate recurrent unit, GRU)模型的故障测距方案。首先，分析频谱能量与故障距离的相关关系，利用小波包分解提取小波包能量谱特征向量，作为GRU模型输入。其次，搭建和训练GRU模型，挖掘时间序列中的深层次故障信息，并利用SSA的迭代寻优对GRU模型参数进行优化，实现故障距离的快速准确定位。最后，在PSCAD/EMTDC 中搭建混合三端直流输电系统模型，实验结果证明该方法定位精度高、抗干扰能力和泛化能力强，并具有一定的耐过渡电阻能力。

Fault location method for a hybrid DC transmission system based on wavelet energy spectrum and SSA-GRU

There are problems of difficult identification of the traveling wave head and difficult extraction of inherent dominant frequency in hybrid DC transmission system fault location. Thus a fault location scheme based on a gate recurrent unit (GRU) model optimized by a wavelet energy spectrum and the sparrow search algorithm (SSA) is proposed. First, the correlation between spectrum energy and fault distance is analyzed, and wavelet packet decomposition is used to extract the wavelet packet energy spectrum feature vector as the input to the GRU model. Second, the GRU model is built and trained to mine deep-seated fault information in the time series, and the parameters of the GRU model are optimized using the iterative optimization of the SSA, so as to realize the rapid and accurate location of the fault distance. Finally, a hybrid three-terminal DC transmission system model is built in PSCAD/EMTDC. The experimental results show that this method has high positioning accuracy, strong anti-interference ability and generalizability, and has a certain resistance to transition resistance.