Research On Maximum Power Point Tracking Control Algorithm In Photovoltaic Power System

Solar energy is one of the new energy sources.It is used in the power generation industry with its universal,broad energy and clean characteristics.It has always been a hot spot for researchers on how to enhance the efficiency of photovoltaic(PV)power generation.Among them,maximum power point tracking(MPPT)is currently a common measure to enhance the operating efficiency of photovoltaic systems.Based on the built photovoltaic system model,the MPPT method based on hybrid quantum particle swarm optimization(HQPSO)is adopted in the thesis for research.The main contents of this thesis are as follows:(1)According to the equivalent circuit of the PV cell,its mathematical model is constructed.Besides,the changes in the output power characteristics of PV cells when the condition alters and the output characteristics of the PV array under the shadow are studied intensively.Through the analysis of the Strengths and weaknesses of traditional MPPT control measures,the deficiencies in multi-peak tracking are obtained.At the same time,the solutions of the existing photovoltaic array under shadow is given a review,and the pros and cons of them are analyzed.(2)The advantages and disadvantages of the MPPT method based on particle swarm optimization are explained.On this basis,a quantum particle swarm method(QPSO)is derived,which has few setting parameters,simple evolution equation and is easy to control.To improve the search ability of the algorithm,the contraction-expansion variable in the quantum particle swarm algorithm is studied,and it is combined with the fitness value to make the algorithm track to the maximum power point faster.The Levy flight strategy is added simultaneously to the evolution equation.The distribution characteristics of the Levy flight are used to add the population diversity of QPSO in the later period of convergence,thereby further improving the reliability of the QPSO in global convergence.The HQPSO is tested through three test functions,and the results show that it performs well in convergence speed and accuracy.(3)After modeling the PV system with different MPPT measures,the MPPT method based on the HQPSO algorithm is compared and analyzed with several methods.In no shadow occlusion,static shadow occlusion and dynamic occlusion,the feasibility of the algorithm in maximum power point tracking is verified.The experimental results show that no matter what the lighting conditions,the MPPT measure based on the hybrid quantum particle swarm algorithm has the ability to track the global maximum power point,and its tracking error is less than 1%,and its convergence time is less than 0.15s.At the same time,this method can adapt well to environmental changes and enhances the efficiency of the PV system better,which has good steady state and dynamic performance.