Direct control scheme based on recurrent fuzzy neural networks for the P-Q decoupled control of UPFC series branch

In this paper, a new control scheme using recurrent fuzzy neural controllers is proposed for the series branch of the unified power flow controller (UPFC) to improve the dynamic performance of real-time power flow control functions with the aim of reducing the inevitable interactions between the real and reactive power flow control parameters. For the purpose of analysing the coupled dynamics of the UPFC and the parameters of the controlled power system model, the equivalent controlled current and voltage sources model is adopted for mathematically modeling the UPFC and the test power system. To simplify the theoretical analysis of the control system the three phase description of a two-bus test power system embedded with a UPFC is transformed into d-q components based on a synchronously rotating reference frame. For the UPFC control systems with inherent nonlinear coupling features, a feed-forward control scheme based on fuzzy neural controllers is developed to realize the decoupling control objectives. Based on the simulation results, the proposed control scheme is able to overcome the drawbacks of the conventional power flow controllers designed on small disturbance linearizing method. Comprehensive simulation results on the EMTDC/PSCAD and MATLAB programs are presented and discussed to verify the effectiveness of the proposed control scheme.