Impact of Dependence on State Identification Results in Distribution Grids Using Copula Theory

Distribution system state identification belongs to one of the most fundamental parts of modern smart grid operation strategies. The quality of identified system states is essential for subsequent algorithms. This paper deals with the impact of dependence on state identification results. Therefore, dependent data samples are generated using Copula theory with different dependence characteristics. The results of multiple Monte Carlo simulations reveal a strong influence of both degree of dependence and dependence structure on state identification errors.