Recurrent Neural Network-based Dynamic Equivalencing in Power System

In stability analysis, only particular areas need to be further detailed simulation and analyzed. It is espected that systems can be simplified and without employing great programming effort or extensive time-consuming computations. In this paper, dynamic equivalent for transmission networks in the interconnected power systems using the recurrent artificial neural netwoks (ANN) is introduced and series parallel model (SPM) structure is chosen to identify equivalent model. The approach depends on measurements of the boundary nodes, need not to postulate any fixed dynamic model in advance. The weighting parameters and structure of ANN will define the parameters and structure of the external areas. This new approach is used to simplify the 39 nodes network of IEEE, the results show the accuracy of the equivalent model and the validity of the proposed method.