Research On Maximum Power Point Tracking And Grid Connection Control Strategy For Photovoltaic Power Generation Systems Under Complex Shadows

The proposal of the "dual carbon" goal further promotes the development of China’s new energy industry and will continue to increase the utilization of green renewable energy such as solar energy and wind energy.Among them,photovoltaic power generation is the main force in the current new energy power generation system.Improving the efficiency of photovoltaic power generation and reducing the harmonic component of grid connected current to meet grid connected requirements have become the focus of current research on photovoltaic power generation systems.Therefore,this article focuses on the maximum power point tracking(MPPT)control strategy and grid connection control strategy of photovoltaic power generation systems,in order to improve the generation efficiency and grid connection stability of photovoltaic power generation systems.The main research content of this article is as follows:(1)A hybrid control strategy based on particle swarm optimization and cuckoo search algorithm(PSO-CS)is proposed to address the issue of traditional MPPT algorithms being prone to local extremum traps in complex shadows and unable to accurately track the maximum power point.The algorithm was simulated and analyzed in three environments:uniform lighting,static shadows,and dynamic shadows.The results showed that the MPPT control strategy based on PSO-CS algorithm can quickly and stably approach the global optimal value for particles,effectively improving the tracking efficiency and stability of MPPT.(2)Aiming at the problem that PSO-CS algorithm is prone to oscillate when tracking the maximum power point process under dynamic shadow,a MPPT control strategy based on improved tuna algorithm(ATSO)is proposed.Firstly,using Tent chaotic maps instead of random parameters in the Tuna Swarm Optimization Algorithm(TSO)enables the algorithm to generate a diverse initial population in the search space;Secondly,the weight coefficients of the spiral foraging strategy are linearized to balance the global and local search performance of the algorithm;Finally,Gaussian distribution estimation is used to replace the parabolic foraging strategy of the original algorithm,thereby improving the convergence rate and search ability of the algorithm and avoiding it from falling into local optima.The performance of the ATSO algorithm was tested using standard test functions,and the algorithm was simulated and analyzed in three environments: uniform lighting,static shadows,and dynamic shadows.The results showed that the ATSO algorithm can avoid the shortcomings of the PSO-CS algorithm in dynamic shadows,and the ATSO algorithm has stronger global search ability and ability to jump out of local extremum.At the same time,the search accuracy and convergence speed are better than the other three algorithms.(3)A voltage and current dual closed-loop control strategy is designed for grid connection control to address the difficulty of achieving zero static error tracking in PI control.The outer loop adopts PI control based on DC side voltage to stabilize the DC bus voltage;The inner loop adopts quasi PR control based on inductance current,which increases the stability of the system.The grid connection simulation analysis of this strategy under uniform lighting and dynamic shadow environment shows that the dual closed-loop control strategy based on PI and quasi PR can achieve stable output of grid connected current and grid connected voltage in the same frequency and phase,and the total harmonic distortion(THD)of grid connected current is less than 5%,which can meet the power quality and stability requirements of photovoltaic grid connection.