Application Of Improved Chaos Optimization Algorithm In Photovoltaic Array MPPT

Photovoltaic power generation systems mainly convert natural light energy into electricity energy required by human beings through photovoltaic arrays.The power-voltage(P-V)characteristic curve of the photovoltaic array has single peak phenomenon in a standard external environment.For this ideal environment,the traditional Maximum Power Point Tracking(MPPT)algorithms,such as CVT? P&O and INC,can achieve better search results.However,the environment in which photovoltaic arrays are located in nature is changing at every moment and it is difficult to maintain a stable standard status.In such a complex environment,the P-V characteristic curve of the PV array output will exhibit multi-peak characteristics.It is easy to fall into the local optimal solution using the traditional single-peak MPPT,and the true global maximum power point cannot be searched.Aiming at the above problems,this paper proposes an adaptive elite strategy to improve the MPPT control strategy of chaotic particle swarm optimization(AEM-CPSO)algorithm,and compares it with the traditional particle swarm optimization(PSO)algorithm and chaotic particle swarm optimization algorithm(CPSO)in Simunlink analysis.This article specifically does the following work:1.Study the principle of photovoltaic cell power generation,compare and analyze the advantages and disadvantages of photovoltaic cell mathematical model,engineering model and kinematics model,select a model that meets the requirements of modeling and simulation,and build the basic module of photovoltaic cell MATLAB/Simulink simulation.2.The simulation experiment was designed according to the photovoltaic cell engineering model to analyze it's output characteristics,and analyze the output characteristics of the series,parallel and series-parallel photovoltaic arrays.3.Four classical MPPT control algorithms of constant voltage tracking,perturb and observe algorithms,incremental conductance and fuzzy logic control are analyzed.The application of intelligent optimization algorithm in MPPT control is introduced.4.This article proposed an adaptive elite strategy to improve the MPPT control strategy of chaotic particle swarm optimization(AEM-CPSO)algorithm.The algorithm performs chaotic search on the first three iterations of the particle,so that the particle has global ergodicity in the initial state.The adaptive elite strategy is applied to the late stage of particle search and is used to alleviate the problem of late oscillation of the algorithm.The photovoltaic MPPT simulation model is built by MATLAB/Simulink,and the simulation analysis is carried out from static and dynamic respectively.It is concluded that the AEM-CPSO algorithm has better global search ability and transient stability than traditional particle swarm optimization and chaotic particle swarm optimization.