Intelligent Fault and Lightning Detection Algorithm for VSC-MTDC grids based on ResNet with Hybrid Attention Mechanism

Abstract

To address the existing challenges in fault detection for voltage source converter-based multi-terminal DC (VSC-MTDC) grids, this paper proposes an intelligent fault and lightning detection algorithm based on S transform and Residual Network with hybrid attention mechanism (RWHAM).The DC line double-ended initial current traveling waves (ICTWs) are first converted into time-frequency matrices by performing S transform and then visualized as a two-dimensional image. The image effectively characterizes the time-frequency features of ICTWs under different fault and lightning conditions, making it easier to extract critical features for the models. Next, the RWHAM model is constructed and the images are fed into the model to detect internal and external faults and lightning interference. The weight of the key information in the image is increased by hybrid attention mechanism, which in turn improves the fault detection ability of the RWHAM model. Extensive simulations involving 21,620 distinct cases on PSCAD/EMTDC validate the algorithm's high accuracy and rapid response across different types of faults and lightning interference. Furthermore, it exhibits excellent generalization ability when the parameters of VSC-MTDC grids change.