Research On Maximum Power Point Tracking Technology In Photovoltaic Power Generation System

Solar energy is one of the most feasible renewable energy at present.It has become an excellent substitute for fossil fuels with the advantages of low pollution and sustainability.However,photovoltaic cells have nonlinear P-V characteristics,which makes their conversion efficiency low and easy to be affected by environmental conditions such as solar radiation and temperature.Therefore,photovoltaic systems need to improve photoelectric conversion efficiency to improve their economic benefits.Therefore,when the temperature and solar radiation inevitably fluctuate,it is very important to find the maximum power point(MPP)from the photovoltaic cell and maintain stable operation at the maximum power point.In the past decades,photovoltaic system maximum power point tracking(MPPT)technology has been widely used in many aspects.This thesis proposes two different MPPT strategies:(1)A MPPT control strategy based on artificial neural network and incremental conductance(ANN-INC)is proposed in this paper to improve the efficiency of the photovoltaic system,In ANN-INC,the output of the trained neural network is transferred to the INC part as the initial duty cycle,which makes the initial duty cycle very close to the duty cycle when the output power of the PV system is maximum,then the INC part can select a small step size to make the output of the photovoltaic system closer to expected output.The strategy has simple structure,fast dynamic response speed,small steady-state power oscillations and high efficiency.The strategy also performs well when the irradiation changes rapidly.The superiority of the strategy is verified in MATLAB/SIMULINK.(2)This section proposes a Hybrid Simulated Annealing and Particle Swarm Optimization(SA-PSO)which is applied in the maximum power point tracking(MPPT)of photovoltaic generation system.SA-PSO combined with Simulated Annealing(SA)and Particle Swarm Optimization(PSO),using SA’s escaping mechanism to improve the shortcomings of PSO slow convergence and easy to fall into the local optimal solution.The overall search ability is increase and the tracking time is reduced.The simulation results use MATLAB/Simulink to compare with P&O MPPT algorithm and PSO MPPT algorithm,the proposed novel technique performs better under shading conditions.Finally,through hardware experimentation,this thesis confirms the SA-PSO algorithm once again.The hardware testing platform is fully operational.The tests were carried out under uniform illumination and partial shading condition scenarios in order to confirm that the algorithm can track the global maximum power point.The hardware results demonstrate that the suggested algorithm may be implemented in a practical setting.Additionally,the SA-PSO and P&O algorithms are also built using the same experimental setup for comparison.