Under dynamic illumination

the peak power of the photovoltaic array varies with the illumination. Traditional MPPT control methods only consider a single peak

resulting in low switching efficiency and poor tracking stability between different power peaks. To address this issue

a distributed photovoltaic multi peak MPPT control method based on an improved PSO algorithm is proposed. Firstly

analyze the U-I characteristics of the system and construct a simplified equation to establish a multi peak MPPT control model; Derive and simplify the U-I characteristic curve equation of distributed photovoltaic power generation system

and construct a control model with the goal of maximizing power. Using particle swarm optimization algorithm to solve the model

optimizing the initial position of particles through Tent mapping

adjusting the inertia weight through adaptive algorithm

and improving the algorithm by replacing fixed parameters with asynchronous learning factors. Experimental verification shows that this method has significant advantages in U-I characteristic analysis accuracy (average absolute error 0.08A

relative error 0.12%)

dynamic tracking efficiency (up to 98.6%

6.2 percentage points higher than traditional methods)

response speed (shortened from 0.25s to 0.12s

52% faster)

and adaptability to complex environments (load satisfaction rate increased from 78% to 95% under 50% local shadow coverage)

providing a reliable solution for efficient operation of photovoltaic systems under dual carbon targets.