Research On MPPT Control Strategy Of Photovoltaic Array Under Local Shadow

In the process of photovoltaic power generation,photovoltaic arrays will inevitably encounter local shadows caused by building shadows,cloud cover and dust cover.When the photovoltaic array is in local shading condition,there will be multiple extreme points on the output power-voltage curve.The traditional maximum power point tracking(MPPT)algorithm may fail at this time and can not accurately find the maximum power point.To solve this problem,it is necessary to study MPPT control strategy with better optimization effect to solve the maximum power point tracking problem under local shadow.Therefore,this paper studies the MPPT control strategy of photovoltaic array under local shadows from two aspects of artificial intelligence optimization and composite control.The main research contents are as follows:Firstly,Aiming at the problem that the maximum power point tracking of traditional particle swarm optimization(PSO)algorithm is easy to fall into local optimum under local shadows,a migration strategy based particle swarm optimization(MISPO)algorithm is proposed.The MIPSO algorithm divides the randomly generated initial particle swarm into several sub-populations,and adopts linear decreasing inertia weight and dynamic change learning factor in the iteration process of each sub-population.Meanwhile,migration strategy and evaluation operator are introduced to evaluate the diversity of sub-populations,and then migration operation is performed on sub-populations with low diversity to enhance information exchange among particles.The experimental results show that the improved particle swarm optimization algorithm has higher accuracy in maximum power point tracking,and improves the convergence speed and tracking accuracy of the algorithm.Secondly,Aiming at the problem that the traditional single MPPT algorithm can not take into account both dynamic and steady-state performance,a composite method of MPPT control strategy is proposed The algorithm combines genetic algorithm and variable step perturbation and observation method,absorbs the advantages of both,and studies the control strategy of MPPT.In the initial stage of control,genetic algorithm is used to search and track the maximum power point quickly.Variable step perturbation and observation method is used to search precisely near the maximum power point so as to stabilize it at MPP.The experimental results show that the composite algorithm is better for the maximum power point tracking under local shadows,and the tracking accuracy and speed are greatly improved compared with the traditional single MPPT algorithm.Thirdly,Based on DSP2812,a small MPPT controller was designed.The hardware circuit includes driving power circuit,voltage and current sampling circuit,driving circuit and RCD buffer circuit.Software design includes system initialization program,AD sampling interrupt program and PWM output interrupt program.The MPPT experimental simulation platform under local shadows is built to verify the feasibility and accuracy of the two different control strategies,which has good application prospects.In conclusion,the improved MPPT algorithm proposed in this paper can accurately track the maximum power point under local shadow,shorten the tracking speed and improve the tracking accuracy,which has certain practical value and research significance...