Study Of Photovoltaic Multimodal Maximum Power Point Tracking Baced On Improved Quantum Particle Swarm Optmization

Photovoltaic power generation,as the world’s most promising green energy,has developed rapidly due to its clean,pollution-free and wide distribution.The photovoltaic maximum power tracking technology can effectively improve the efficiency of photovoltaic power generation systems,and has been widely used.However,when the operating environment of traditional photovoltaic tracking technology changes,the tracking efficiency and power supply quality will be greatly affected,especially when the large-scale photovoltaic array is affected by partial shading,not only the power generation efficiency and quality are affected,but also extreme operating conditions.It can also cause serious fire and other safety issues.In order to improve the photovoltaic power generation efficiency and power supply quality in complex environments,this paper uses the MPPT algorithm combined with an improved quantum particle swarm algorithm and an incremental conductance method to achieve photovoltaic maximum power tracking.And the dynamic quality and robustness of the system.This paper first introduces the current research status of photovoltaic system maximum power tracking technology and existing MPPT conventional algorithms;establishes a photovoltaic cell model,and discusses the output characteristics of photovoltaic cells under two conditions of uniform light and partial shade.On this basis,the quantum particle swarm optimization(DCWQPSO)optimization algorithm with adaptive adjustment of inertia weight is used to improve the search range of algorithm particles by introducing β value mutation,which effectively improves the problems of particles falling into local extremes.A photovoltaic maximum power tracking control algorithm that combines Levi’s flight strategy with DCWQPSO algorithm(LF-DCWQPSO)is proposed to reduce the possible problems of particles falling into local extremes in the late evolution process.In the MATLAB/Simulink environment,a DC/DC converter model of a photovoltaic power generation system was built,and a photovoltaic array and a control module were constructed.The effectiveness of the LF-DCWQPSO algorithm in photovoltaic maximum power tracking is verified through simulation,and the problem that particles easily fall into local extremes and premature convergence is achieved,and the system’s requirements for fastness are achieved.Aiming at the problem of oscillation of LF-DCWQPSO algorithm during dynamic tracking,a photovoltaic maximum power tracking(MPPT)control algorithm combining LF-DCWQPSO algorithm and INC algorithm was proposed.The algorithm uses the LF-DCWQPSO algorithm for global search of the maximum power point,and then uses the INC algorithm to track the maximum power point locally.Simulation verification shows that the organic combination of the LF-DCWQPSO algorithm and the INC algorithm solves the oscillation problem in the convergence process of the LF-DCWQPSO algorithm.Theoretical analysis and simulation results show that the method proposed in this paper can effectively respond to changes in environmental uncertainties such as lighting,and can quickly and stably search the global maximum power point,which improves the maximum power tracking efficiency of photovoltaic power generation systems under uncertain environments and has good performance,dynamic quality and robustness.