电测与仪表2024，Vol.61Issue(5)：166-174,9.DOI:10.19753/j.issn1001-1390.2024.05.023
基于改进DDAE的风电场集电线单相接地故障测距
Single-phase grounding fault location of wind farm collector based on improved DDAE
摘要
Abstract
In order to solve the problem that it is difficult to accurately locate the collector line after short-circuit of hybrid connection in wind farm,a fault location method based on improved deep denoising auto-encoder(DDAE)network is presented in this paper.By analyzing the zero-sequence current on collector line faults,it is known that the transient current value,steady-state current amplitude,steady-state current phase and fault distance are strong-ly non-linear,and the precise location of collector line is achieved by deep learning mining this complex relation-ship.A distance regression output port is added to the deep denoising auto-encoder network,and joint training is used to improve the accuracy,noise resistance and robustness of the positioning network.Firstly,the hub model is built with PSCAD/EMTDC,and fault zero-sequence current sequence and corresponding distance in a given time window are used as fault samples to simulate different failure cases to generate sample sets.Then,an improved deep auto-encoder network is trained on the training set to obtain an optimal network for precisely measuring the fault distance.With the help of the zero-sequence current amplitude relationship of each measurement point,the fault area can be determined first,and the precise location of the fault can be determined by feeding the fault sam-ples into the trained network.This method proposed in this paper has a good adaptability to multi-branch and hy-brid short lines of collector lines.Location performance is significantly better than traditional machine learning algo-rithms,as well as less affected by transition resistance,sampling rate,noise,fault phase angle.关键词
深度去噪自编码/风电场/集电线路/故障定位Key words
DDAE/wind farm/collector line/fault location分类
信息技术与安全科学引用本文复制引用
朱永利,刘富州,张翼,郑艳艳..基于改进DDAE的风电场集电线单相接地故障测距[J].电测与仪表,2024,61(5):166-174,9.基金项目
国家自然科学基金资助项目(71701087) （71701087）
