通过基于RFID数据收集和深度学习的多项式近似计算,微电网发生器的转子角度稳定性.
Wajid Khan1, Muhammad Zain Yousaf2,3,4, Arvind R Singh5
1School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
Scientific reports
|November 16, 2024
概括
这项研究通过修改的加勒金方法 (MGM) 和RFID数据增强了微电网稳定性评估. 这种新的方法提高了故障检测的准确性和速度,以实现更安全的微电网运行.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 控制系统 控制系统
背景情况:
- 由于依赖假设,传统的转子角度稳定方法与在线短暂稳定性测试作斗争.
- 微电网需要强大的在线稳定性评估,以确保可靠运行.
- 微电网中的数据冗余和噪声可能会阻碍准确和及时的故障诊断.
研究的目的:
- 为微电网转子的角度稳定性评估提出一个增强的修改Galerkin方法 (MGM).
- 整合射频识别 (RFID) 技术,用于实时数据采集和改进故障诊断.
- 开发一种复杂的故障诊断系统,使用混合CNN-LSTM模型和信号处理技术.
主要方法:
- 改进的加勒金方法 (MGM) 使用多项式近似的微电网动态行为的增强.
- 集成实时RFID数据采集,以减轻冗余数据和噪声的问题.
- 应用信号处理技术 (福里埃变换,时间域特征,总波扭曲 - THD) 和特征提取用于故障识别.
- 开发一种混合卷积神经网络-长期短期记忆 (CNN-LSTM) 模型用于故障分类.
主要成果:
- 拟议的方法显著提高了故障检测效率和精度.
- 实现了0.94的分类准确度,超过了现有方法.
- 通过对IEEE三机九巴系统进行广泛的案例研究,证明了故障检测速度和准确性的可观察到的改进.
- 验证了RFID集成和混合CNN-LSTM模型在增强诊断能力方面的有效性.
结论:
- 增强的MGM与RFID技术相结合,为评估微电网中的旋转器角度稳定性提供了一种卓越的方法.
- 开发的故障诊断系统提高了微电网运行的安全性和可靠性.
- 这种方法有效地解决了动态和实时稳定性测试中传统方法的局限性.
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