Using an Improved Joint Normal Transform Method for Modeling Stochastic Dependence in Power System

Abstract

The dependence factors in power systems should be considered in stochastic power flow computation, so Joint Normal Transform (JNT), belonging to the copula function technology, is improved to model these dependences. Firstly, the procedure of traditional JNT method is introduced and the principle of correlation structure's remaining unchanged is analyzed combined with the properties of rank correlations when JNT method is utilized in dependence modelling. Then, an improved JNT sampling method is proposed to raise sampling efficiency by applying Orthogonal Transformation according to the characteristic that JNT method is based on Normal Distribution. Finally, a calculation example is designed to verify the feasibility of the proposed improved JNT sampling method.