AI 中文总结
提出基于S变换和混合注意力机制ResNet的VSC-MTDC电网智能故障与雷电检测算法，通过时频图像识别与21,620个案例仿真验证，实现高精度快速检测及良好泛化。
AI 中文摘要
为解决基于电压源换流器的多端直流（VSC-MTDC）电网在故障检测方面存在的现有挑战，本文提出了一种基于S变换和混合注意力机制残差网络（RWHAM）的智能故障与雷电检测算法。首先，将直流线路双端初始电流行波（ICTWs）通过S变换转换为时频矩阵，并将其可视化为二维图像。该图像有效表征了不同故障和雷电条件下ICTWs的时频特征，便于模型提取关键特征。其次，构建RWHAM模型，并将图像输入模型以检测区内故障、区外故障及雷电干扰。通过混合注意力机制增强图像中关键信息的权重，进而提升RWHAM模型的故障检测能力。在PSCAD/EMTDC平台上进行的涉及21,620个不同案例的广泛仿真验证了该算法在不同类型故障和雷电干扰下的高准确性和快速响应能力。此外，当VSC-MTDC电网参数变化时，该算法表现出优异的泛化能力。
英文摘要
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.
