Yousaf Muhammad Zain, Singh Arvind R, Khalid Saqib, Bajaj Mohit, Kumar B Hemanth, Zaitsev Ievgen
School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan, 442002, China.
Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang Unversity, Zhuji, 311816, Zhejiang, China.
Sci Rep. 2024 Aug 2;14(1):17968. doi: 10.1038/s41598-024-68985-5.
As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model's training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.
随着欧洲将更多可再生能源，特别是海上风电，整合到其超级互联电网中，对可靠的长距离高压直流（HVDC）输电系统的需求激增。本文探讨了基于模块化多电平换流器（MMC）构建的高压直流系统的复杂性，特别是关于直流故障电流的快速上升。我们提出了一种仅通过采用集成离散小波变换（DWT）进行特征提取的长短期记忆（LSTM）网络对直流输电线路进行故障识别和分类的新方法。我们基于LSTM的算法在具有挑战性的环境条件下有效运行，确保高抗故障检测能力。一个独特的具有多个时间窗口（1毫秒、1.5毫秒和2毫秒）的三级继电系统可确保在大范围内进行准确的故障检测。采用贝叶斯优化进行超参数调整，简化了模型的训练过程。研究表明，我们提出的框架对外部故障和干扰具有100%的恢复能力，在各种测试场景中平均识别准确率达到99.04%。与依赖多个手动阈值的传统方案不同，我们的方法利用单个智能调谐模型来检测高达480欧姆的故障，提高了直流电网保护的效率和鲁棒性。
