Uncertainty analysis of load model based on the sparse grid stochastic collocation method

Abstract

There are a lot of uncertainties in load modeling and it parameter solutions, which is difficult to estimate uncertainty with traditional methods if the number of parameters is immense. This paper adopts the sparse grid stochastic collocation method for uncertainty analysis, and proposes a strategy available to calculate the multi-parameter uncertainty arising from load models. For multiple random inputs, sparse grid method can be regarded as an extension of Gaussian quadrature formulas in multi-dimensional cases. Based on the sparse grid stochastic collocation method, the collocation points can be selected among the Gaussian points of (l+1) order and lower than (l+1) order. Compared to other probabilistic analysis methods, it can not only maintain the integral precision but avoid the exponential rise of collocation points, and can greatly reduce simulation time. The case study on multiparameter uncertainty of the composite load model verifies the integral precision and the validity of the proposed method.