With the continuous development of new energy technology, photovoltaic power generation is more and more widely used. As one of the key components of photovoltaic power generation system, the stability control of three-phase grid connected inverter is the key to realize photovoltaic grid connection. The traditional control methods of grid connected inverter include PID control, PR control and deadbeat control. These control methods generally rely on accurate mathematical models, and have poor robustness and anti-interference. In this paper, a control strategy using double closed-loop sliding mode control (SMC) is proposed. The strategy uses the outer voltage loop and the inner current loop to stabilize the output voltage and current respectively. Both the inner and outer loops use integral sliding mode control to enhance the robustness and anti-interference of the system. There are some unknown parameters in the design of sliding mode controller. The more appropriate these parameters are set, the smaller the chattering of the controller, the faster the approaching speed and the stronger the anti-interference. However, the current research on sliding mode control of grid connected inverter mostly lacks the parameter setting process of the controller itself. The selection of parameters mainly depends on the trial and error method. The controller designed in this way can not guarantee the optimal effect and the efficiency is relatively low. In this paper, a multi-objective particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the sliding mode controller. Taking the grid connected current quality, grid connected speed and overshoot as the optimization objectives, the parameters of the sliding mode controller are accurately adjusted, which makes the controller have the advantages of high tracking accuracy, fast response speed and small overshoot. Finally, the superiority and feasibility of the proposed strategy are proved by Matlab / Simulink simulation experiments.
 

grid connected inverter; Sliding mode control; Particle swarm optimization algorithm; Parameter setting; Multi objective optimization