Application Research Based On Improved QPSO Algorithm In MPPT

There will be non-uniform illumination in the solar photovoltaic system during the generation process,which will result in multiple peaks in the P-U characteristic curve of the photovoltaic module output.Conventional maximum power point tracking(MPPT)technique has negligible control effect under the localized shadow condition of the solar panel,and it is difficult to trace and output at the maximum point of the multi-peak P-U characteristic curve.At the present stage,there are two main methods to solve such a search for untoward problem: The first,improved heuristic algorithm is used to command MPPT to enhance the search results;The second,to modify the photovoltaic array to enhance its applicability to the environment and is not affected by metabolic external factors.This article studies in the first way,aiming to develop heuristic algorithm with a better effect and apply it to MPPT control to guarantee that the power output always stays at the maximum value.The MPPT of the local shadow state is a nonlinear problem with discrete characteristics.Some traditional heuristic algorithms have better effect in dealings with continuous problems,but for discrete problems,premature convergence is facility to occur.The searched peaks are not globally optima valuel,thus resulting in waste of electrical energy.In view of this situation,in this article,the search method of chaotic descending weight strategy is used to improve the quantum particle swarm optimization(QPSO)algorithm to improve the tracking effect.In this installment,Firstly,the fundamental current situation of photovoltaic power generation and the application of maximum power tracking method are introduced.Secondly,the power generation principle of solar photovoltaic cells is remomended,the mathematical model of photovoltaic cells is established,and the general mathematical model of photovoltaic array is presented.Thirdly,introduced the principle of maximum power tracking.Furthermore,three familiar MPPT search methods and their advantages and disadvantages are expound ed.Fourthly,on basis of the quantum particle swarm,the ameliorate is proposed and detection to illustrate the effectiveness of the improved algorithm.Finally,the Matlab/Simulink simulation platform was used to build the simulation model of MPPT system under different lighting conditions.The quantum particle swarm optimization(QPSO)algorithm and the improved quantum particle swarm optimization(LDQPSO)algorithm are respectively applied to MPPT control.The validity of the proposed algorithm is verified through comparative analysis.