Model analysis and real-time implementation of model predictive control for railway power flow controller

Abstract Railway power flow controllers (RPFCs) are known as a power electronics based full-fledged compensator in the field of electric railway systems (ERSs). They have attracted experts attention over the last years due to their outstanding performance in dealing with power quality (PQ) problems. The complexity, randomness and uncertainty characteristic of ERS and traction loads have led the control of RPFC to be difficult. In this paper, a precise modeling method based on space-state model (SSM) and dynamic switching pattern is presented. Applying the extracted equivalent model and mathematical equations, a modified model predictive control (MMPC) method is proposed to improve the compensation and dynamic performance of RPFC. To eliminate the low frequency oscillation (LFO) and other external disturbance effects, a dual-loop modified d-q frame control is implemented with frequency locked capability. Precise simulations have been provided to verify the modeling and proposed control procedure performance. Moreover, a real-time laboratory setup based on RT-LAB and Opal-RT is provided to confirm the simulation results and theoretical analysis according to the desired specifications of the real ERS.