一个kurtosis-ESPRIT算法用于实时稳定性评估在垂落控制的微电网.
Adham Osama1, Abdallah F El-Hamalawy2, Mohammed E Ammar2
1Advanced Power and Energy Center (APEC), Electrical Engineering Department, Khalifa University, Abu Dhabi, UAE. 100059837@ku.ac.ae.
Scientific reports
|January 13, 2025
概括
本研究介绍了通过旋转不变技术 (ESPRIT) 对信号参数的库尔托斯估计,用于分析基于逆变器的微电网 (IBMGs). 该方法使用实时数据准确识别主导模式,在各种条件下优于现有技术.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 控制系统 控制系统
背景情况:
- 现有的垂落控制微电网分析模型是计算密集的,无法考虑实时参数和操作变化.
- 基于逆变器的微电网 (IBMGs) 的准确实时分析对于稳定性和性能至关重要.
- 主导模式的识别对于理解微电网动态至关重要.
研究的目的:
- 提出一种新的方法,即通过旋转不变技术 (ESPRIT) 来估计信号参数的库尔托斯估计,用于识别垂落控制的IBMG中的主导模式.
- 使用本地实时测量进行模式估计,克服复杂分析模型的局限性.
- 为评估信号特征和模式突出性引入一种度度量.
主要方法:
- 一个短时间的干扰被应用到一个选定的分布式发电机 (DG) 的主动功率下降收益.
- 主导系统模式是从使用ESPRIT算法的本地实时测量中估计的.
- 库尔托斯测量用于评估信号质量和估计模式的意义.
主要成果:
- 与Prony,矩阵笔和子空间识别技术相比,拟议的Kurtosis-ESPRIT方法显示出更高的估计准确性.
- 该算法在杂的环境中,在不同的负载条件下和不同的网络配置下表现出强大的性能.
- 通过 MATLAB / SIMULINK 模拟和 OPAL-RT 控制器在循环实验的验证证实了有效性和实时适用性.
结论:
- 库尔托西斯-ESPRIT方法提供了一个有效和计算效率高的解决方案,用于实时主导模式识别在垂落控制的IBMGs中.
- 该方法的稳定性和准确性使其适用于实际的微电网监控和控制应用.
- 该技术提高了在动态运行条件下分析和管理微电网稳定性的能力.
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