基于随机矩阵理论的电网静态稳定态势评估方法

A Method for Power System Steady Stability Situation Assessment Based on Random Matrix Theory

基于广域测量信息的电力系统静态稳定态势评估具有非常重要的现实意义。该文在分析电力系统实际运行所产生的数据基础上,提出一种基于随机矩阵理论的大数据融合方法。基于随机矩阵理论评估电网静态稳定态势的方法具有无需详细的物理模型、又可综合考虑历史数据和实时数据,以及可从高维角度认识复杂系统等优点。通过对两种极限谱分布函数M-P率(Marchenk-Pastur law)和圆环率在静态稳定态势评估应用的原理说明,提出采用线性特征根统计量平均谱半径作为评估指标;对IEEE39节点系统的仿真,验证了所提方法的有效性,为静态稳定态势评估提供了一种新思路。

Power system steady stability situation assessment based on the wide were measurement system （WAMS） has great practical significance. The actual operation data generated by the power system are analyzed and a big data fusion method for steady stability situation assessment based on random matrix theory was proposed. The two limit spectral distribution functions （Marchenk-Pastur law and ring law） in power system steady stability situation assessment principle were proposed. On this basis, the linear eigenvalue statistic, such as mean spectral radius, was calculated as assessment indicator. Simulation based on IEEE 39 system verified the effectiveness of the method. The method can be used for steady stability situation assessment of power grid and it has the following advantages： unnecessary to build detailed physical models; able to consider the historical data and real-time data at the same time, and see the complex systems from the perspective of high-dimensional, etc.