A Nonparametric Regular Vine Copula Model for Multidimensional Dependent Variables in Power System Reliability Assessment

Copula function has merit in formulating the marginal distribution and dependence structure of multidimensional random variables separately, and gains increasing attention in the probabilistic risk analysis of power system. Although Copula function does well in dependence modeling for two-dimensional case, it encounters problem of computational complexity and modeling accuracy for high dimensional scenarios. This paper proposed a nonparametric regular vine copula model, which converts the high- dimensional Copula function into the product of some bivariate Copulas functions. Moreover, the parameters of these bivariate Copulas were estimated based on data-driven nonparametric method. Furthermore, in order to avoid the problem in nonparametric estimation that the distribution range of the estimated Copula exceeds its feasible domain, a probability distribution transformation method was also presented. The dependence model of multivariate can be modeled flexibly and accurately by the proposed method, and its validity was verified by case studies. © 2020 Chin. Soc. for Elec. Eng.