Fault Identification for Flexible DC Distribution Line Based on Improved Deep Residual Network
An improved deep residual network(ResNet)fault identification method for flexible direct current(FD)transmission lines is proposed to address the issues of poor anti-noise performance and insufficient performance with limited historical samples in existing deep learning methods.Firstly,the residual unit is enhanced by integrating attention mechanisms and regularization methods,and the model is optimized in terms of width and depth.Then,feature screening of the fault electrical quantities is conducted using recursive feature elimination(RFE)within cross-validation to define key features based on feature attributes.Finally,a simulation model of the Zhangbei-Beijing four-terminal±500 kV FD transmission network is established on the PSCAD simulation platform.The effectiveness of the proposed method under anti-noise interference and small sample size conditions is verified through simulations.
flexible DC transmission systemResNetattention mechanismfeature selectionfault identification
万方数据
维普
