基于数据驱动的区域惯量估计方法在WECC电力系统上的比较研究
Comparative Study of Data-driven Area Inertia Estimation Approaches on WECC Power Systems
AI总结：
本文在WECC 240节点系统上比较了系统辨识、测量重构和机电振荡三类区域惯量估计方法，发现基于系统辨识的方法鲁棒性和准确性最优。
摘要：
随着基于逆变器的资源越来越多地并入电网，系统惯量显著降低，可能危及频率稳定性。为评估现有区域惯量估计技术对实际电力系统的适用性，本文对系统辨识、测量重构和基于机电振荡的区域惯量估计方法进行了严格的比较分析，并具体应用于大规模多区域的WECC 240节点电力系统。综合结果表明，基于系统辨识的方法相较于其他方法表现出更优的鲁棒性和准确性。
英文摘要：
With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniques for real-world power systems, this paper presents a rigorous comparative analysis of system identification, measurement reconstruction, and electromechanical oscillation-based area inertia estimation methodologies, specifically applied to the large-scale and multi-area WECC 240-bus power system. Comprehensive results show that the system identification-based approach exhibits superior robustness and accuracy relative to its counterparts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
