基于贝叶斯推理的电力系统转动惯量和新能源虚拟惯量估计方法

Estimation method of rotational inertia of power system and virtual inertia of new energy based on Bayesian inference

在新型电力系统背景下,以风光为代表的可再生能源导致系统惯性水平低、不确定性强,电网频率稳定问题凸显。基于虚拟惯量控制的新能源虽然在一定程度上提升了低惯量电网的频率稳定性,但同时增加了电网惯量评估难度。针对传统电网在线惯量监测方式难以准确估量同步机转动惯量和新能源虚拟惯量的问题,文章在无需任何线性假设的前提下,提出一种基于多重重要性采样-贝叶斯推理的电力系统转动惯量和虚拟惯量综合估计方法。基于相量测量单位(PMU)局部测量信息和贝叶斯推理框架,通过多重重要性采样算法抽样获得惯量参数的非高斯后验分布,从而保证惯量估计准确性。仿真结果表明,该方法在同步和非同步发电机的在线惯量估计方面均具备较高的精度,可推广应用于以新能源为主导的新型电力系统。

In the context of new power systems,represented by renewable energy sources such as wind and solar,low system inertia and high uncertainty have led to prominent issues with grid frequency stability.While new energy sources with virtual inertia control have improved frequency stability to some extent in low-inertia grids,they have simultaneously increased the difficulty of inertia assessment in the grid.Addressing the challenge where traditional online inertia monitoring methods struggle to accurately estimate synchronous machine rotational inertia alongside virtual inertia from new energy sources,this paper proposes a comprehensive estimation method for rotational and virtual inertia in power systems based on multi-importance sampling and Bayesian inference without requiring any linear assumptions.This approach utilizes local measurements from PMUs(Phasor Measurement Units)within a Bayesian inference framework and employs multiimportance sampling algorithms to sample from the non-Gaussian posterior distribution of inertia parameters,ensuring the accuracy of inertia estimation.Simulation results demonstrate that this method exhibits high precision in online inertia estimation for both synchronous and asynchronous generators and can be widely applied in novel electric power systems dominated by new energy sources.