Aiming at the problem of power coupling and complicated decoupling in the d-q coordinate system of a three-phase grid-connected inverter, a current closed-loop control strategy based on an improved QPIR (quasi-proportional integral resonant) controller in the α-β two-phase static coordinate system is proposed. Firstly, the mathematical model of an LCL three-phase grid-connected inverter is established, and its instantaneous power calculation equation is deduced. Secondly, the frequency method is applied to compare and analyze the proportional resonant, quasi-proportional resonant, and improved current controller, and the appropriate improved controller parameters are obtained according to the traditional proportional integral controller parameter design method and the weight coefficient. Finally, the improved controller is compared with the traditional controller in the simulation model of the LCL three-phase grid-connected inverter based on active damping. The results show that the proposed improved current control strategy has good dynamic response characteristics, can realize the non-static error control of grid-connected current, and realizes the decoupling control of active power and reactive power when the load jumps. At the same time, the results also prove the superiority of the proposed control strategy and verify its effectiveness.

A multi-rate parallel real-time simulation method based on FPGA–CPU is studied to realize the asynchronous co-simulation of the converter of doubly fed wind power systems with the wind turbine and external power grid. The doubly fed wind power system is partitioned by simulation step length, and the partitioned small-step-length data are processed using integral homogenization. For large-step data, an improved delay-compensated linear interpolation method combined with Newton interpolation is proposed for processing. The general small time-step (GST) model method is used to implement the FPGA modeling of the small-step converter, and resource optimization is achieved through timing time-division multiplexing. Asynchronous parallel co-simulation of a doubly fed wind power system is implemented on an FPGA–CPU co-simulation platform. Among them, the FPGA realizes the development of the converter HDL with a small step of 1 $\mathsf{\mu }\mathrm{s}$, while the CPU completes the simulation of the wind turbine and power grid synchronously with a large step of 50 $\mathsf{\mu }\mathrm{s}$. Finally, by comparing with MATLAB/Simulink offline simulation and analyzing the error, it is concluded that the simulation accuracy of the improved method in this paper is higher than that of the un-interpolated parallel simulation, which verifies the real-time performance and accuracy of the modeling and improved method in this paper.

Real-time online simulation based on a real-time workshop (RTW) plays a vital role in the study and application of power electronics. However, restricted by the performance of equipment and hardware, the simulators so far available in the market mainly support simulation steps over 50 μs, while large step simulation may result in the action delay of pulse-width modulating (PWM), numerical oscillation and high-level non-characteristic harmonic distortion. In view of these problems, this paper puts forward a modeling method based on integral prediction and interpolation compensation. First of all, prediction is performed one step in advance by the implicit trapezoidal method to find out the accurate time when the triangle carrier wave intersects with the modulation wave. At the same time, a mathematic model is built for the insulated gate bipolar transistor (IGBT) to output equivalent voltage waveform according to the principle of area equivalent. Next, in MATLAB/Simulink, offline simulation is performed with the three-phase AC-DC-AC converter as the subject. By comparing the control accuracy, the content of harmonic wave and the simulation time, the simulation effects of the 50 μs fixed-step interpolation prediction model are the same as that for a 5 μs fixed-step standard model. Finally, the effectiveness and high efficiency of this algorithm are verified on a real-time simulator, marking the application of offline models on real-time simulators.