贝叶斯优化的LSTM-DWT方法用于基于MMC的HVDC系统的可靠故障检测
Muhammad Zain Yousaf1,2, Arvind R Singh3, Saqib Khalid1,2
1School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan, 442002, China.
Scientific reports
|August 2, 2024
概括
一种新方法使用长短期内存 (LSTM) 网络和离散波段变换 (DWT) 准确检测高压直流 (HVDC) 传输线路的故障. 这种先进的系统确保了可靠的电网保护,精度为99.04%,即使在具有挑战性的条件下.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 整合可再生能源的整合
背景情况:
- 欧洲越来越依赖海上风力发电,需要强大的长距离高压直流 (HVDC) 传输.
- 模块化多级转换器 (MMC) 对现代高压直流系统至关重要,但随着直流故障电流的快速增加而面临挑战.
- 现有的故障检测方法往往难以准确,需要手动调整值.
研究的目的:
- 开发一种新的,高精度的故障识别和分类方法,用于HVDC输电线路.
- 为了应对基于MMC的HVDC系统中快速增长的直流故障电流的挑战.
- 提高直流电网防护对各种故障和干扰的效率和稳定性.
主要方法:
- 长短期内存 (LSTM) 网络与离散波段变换 (DWT) 集成,用于特征提取和故障分类.
- 开发一个具有多个时间窗口 (1 ms,1.5 ms,2 ms) 的三级继电系统,用于在长距离上精确检测故障.
- 贝叶斯优化的应用,以实现LSTM模型高效的超参数调整.
主要成果:
- 拟议的LSTM-DWT框架在各种测试场景中实现了99.04%的平均识别准确率.
- 该系统证明了100%的抗外部故障和干扰的弹性,证明了它的稳定性.
- 该算法成功检测到高达480欧姆的故障,优于依赖于多个手动值的传统方法.
结论:
- 基于LSTM-DWT的新型故障检测系统为保护HVDC输电线路提供了高度准确和强大的解决方案.
- 这种智能单一模型方法提高了直流电网保护的效率和可靠性,特别是在综合可再生能源系统中.
- 拟议的方法在传统故障检测方案上取得了显著的进步,提供了卓越的性能和适应性.
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