惯性估计算法（IEAs）在提供适当频率响应中的比较分析
Comparative Analysis on Inertia Estimation Algorithms (IEAs) in Providing Proper Frequency Response
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中文总结 AI 辅助
本文系统比较了多种惯性估计算法（滤波、拟合、RLS、KF）在频率响应中的应用，并探讨其对风能惯性仿真策略的影响，强调需鲁棒、自适应、数据驱动的估计框架以确保低惯性电网安全运行。
中文摘要 AI 辅助
惯性常数H[s]是电力系统韧性的基本指标，它将发电与负荷之间的功率不平衡与频率偏差联系起来。在现代电力系统中，惯性常数对于稳定性约束最优潮流（OPF）下的频率备用调度、辅助服务中的需求响应（DR）、系统解列以及频率控制至关重要。虽然惯性常数传统上定义为同步发电机在公共基值上的固有动能，但这忽略了HVDC和基于逆变器的资源（IBRs）内部非线性动力学和控制下的可释放功率。因此，准确的实时惯性估计对于执行适当的频率控制和指示频率恢复失败的风险至关重要。然而，在事件驱动的参数跳变和局部暂态响应下，噪声频率测量使得这一任务具有挑战性。本文对用于频率响应应用的惯性估计算法（IEAs）进行了系统的比较分析。基于测量的方法中的经典方法（如滤波和拟合）与基于数据的参数估计技术（如递归最小二乘法（RLS））以及基于模型的方法（如卡尔曼滤波（KF））进行了基准比较。主要贡献包括：（i）对基于模型和基于数据的惯性估计方法进行了全面综述，（ii）探讨了IEA对基于风能的惯性仿真策略的影响。研究结果强调了需要鲁棒、自适应和数据驱动的估计框架，以确保未来低惯性电网的安全运行。
英文摘要
The inertia constant H[s] is a fundamental indicator of power system resilience, linking power imbalance between generation and load to frequency deviation. It is essential in frequency reserve dispatch under stability-constrained optimal power flow (OPF), demand response (DR) in ancillary service, system decoupling and frequency control in modern power system. While the inertia constant is traditionally defined as the intrinsic kinetic energy of synchronous generators on bar normalized to the power base, this neglects the releasable power under nonlinear dynamics and control inside HVDC and Inverter-based Resources (IBRs). Accurate real-time inertia estimation is therefore essential to perform proper frequency control and to indicate the risks of failure in frequency restoration. It, however, is challenging with noisy frequency measurement under event-driven parameter jumps and locational transient responses. This paper presents a systematic comparative analysis on inertia estimation algorithms (IEAs) for frequency response applications. Classical methods such as filtering and fitting under measurement-based methods are benchmarked against data-based parameter estimation techniques such as recursive least squares (RLS) and model-based methods such as Kalman filtering (KF). The main contributions are: (i) a holistic review of model- and data-based inertia estimation methods, (ii) exploration on the effect of IEA to wind-based inertia emulation strategies. The findings underscore the need for robust, adaptive, and data-driven estimation frameworks to ensure secure operation of future low-inertia grids.
发表机构
- The University of Hong Kong（香港大学）
- CLP Power Hong Kong（中华电力有限公司）
机构由 AI 辅助整理，请以论文原文为准。
