A Probabilistic-Voronoi Adaptive High-dimensional Model to Evaluate Small Signal Instability Risk of Renewable Power System
Uncertainties from renewables and loads have brought huge challenges to the power system small signal stability.This paper proposes a high-dimensional model expression approach based on probabilistic-Voronoi adaptive sampling method to assess the risk of renewable power system small signal instability.First,fundamental steps and algorithms to apply high-dimensional model expression method are briefly introduced,followed by the influence analysis of the solution method and collocations on its accuracy.Then,characteristics of the moving least squares method are analyzed,and a dynamic radius selection method is proposed to improve the accuracy and efficiency.Then,the adaptive Voronoi sampling method is improved and combined with the probability idea to obtain the probability-Voronoi sampling method,improving the efficiency of risk assessment modeling sampling.Thus,combined with dynamic radius moving least squares method and probability-Voronoi sampling method,a high-dimensional model expression method for the small signal instability risk assessment is established.Finally,by simulation tests on several renewable power system test systems,the accuracy and efficiency of the proposed method are compared and verified.The practicability of this research is also verified by the actual source-load uncertain data and power grid structure.
new energyhigh dimensional model representationsmall signal stabilityVoronoiadaptive
万方数据
维普
