基于sViT的风电场集电线故障区段定位

Fault Section Location of Wind Farm Collector Line Based on sViT

为解决风电场集电线单相接地故障后定位困难的问题,提出基于变分模态-小波变换(VMD-CWT)时频谱联合孪生视觉自注意力模型(sViT)的故障区段定位方法。分析发现故障区段与集电线故障电压的VMD-CWT谱有密切关系,借助深度学习算法挖掘谱线与故障区段的关系可以实现集电线故障区段定位。借助PSCAD/EMTDC软件搭建集电线模型,收集各类故障情况的数据后进行VMD-CWT变换生成时频谱;在训练集上搜索sViT网络的最优识别参数,将这一网络的分支用于测试集识别。仿真结果表明该方法对集电线多分支、混合短线有着良好的适应能力,定位受到过渡电阻、噪音和故障相位角的影响较小。

In order to solve the problem of difficult location after a single-phase grounding fault in the wind farm collector line,a fault section location method based on the Variational Mode Decomposition-Continuous Wavelet Transform(VMD-CWT)time frequency spectrum combined with siamese Vision Transformer(sViT)is proposed.It is found that the fault section is closely related to the VMD-CWT spectrum of the fault voltage of the collector,the fault section location of the collector line can be realized by mining the relationship between the spectral line and the fault section by using the deep learning algorithm.With the help of PSCAD/EMTDC software to build the collector line model,collect the data of various fault conditions,and generate the time frequency spectrum of VMD-CWT transformation;the optimal recognition parameters of the sViT network will be found on the training set,and the branch of this network will be used for test set recognition.The simulation shows that the method has good adaptability to multi-branch collector lines and mixed short lines,and the positioning is less affected by transition resistance,noise and fault phase angle.