Study On Particle Swarm Tracking Algorithm For Shrinking Maxi Mum Power Point In Photovoltaic Power Generation

As the main form of using solar energy,photovoltaic power generation has the advantages of environmental protection,no pollution,and long service life of a single investment.However,the amount of power generation varies with the weather,and has a strong randomness.When the output power of the photovoltaic power generation system fluctuates greatly,the traditional maximum power point tracking algorithm will fail to track,resulting in a reduction in power generation efficiency.Therefore,how to accurately track the maximum power point in complex situations is of great significance to improve the efficiency of power generation.The main research work of this article is as follows:(1)Research on the working principle of photovoltaic power generation system.By studying the overall structure of the photovoltaic power generation system,the three DC / DC conversion circuits are compared,and the Boost circuit is established as a DC / DC conversion circuit.The circuit can effectively increase the size of the output voltage,and the driving circuit is relatively simple and easy to operate.By analyzing the working principle of photovoltaic cells,a dual diode model of photovoltaic cells was established and a simulation model was built in Matlab / Simulink to analyze the output characteristics of photovoltaic cells.Build a4 × 2 photovoltaic array simulation model,and adjust the light and temperature to analyze the impact of the sudden change of the external environment on the output characteristics of the photovoltaic array.(2)Research on traditional maximum power point tracking algorithm.The tracking process of constant voltage method,disturbance observation method and conductance incremental method are studied,and the advantages and disadvantages of the three algorithms are compared.Although the traditional MPPT algorithm is simple in logic,it can quickly track to the maximum power point under the condition of constant illumination.However,due to the limitation of detection accuracy and the algorithm's own defects,the algorithm will track the direction of tracking when the illumination changes rapidly.Judgment errors lead to tracking failure.(3)Research on shrinking particle swarm algorithm.Aiming at the situation that the particle swarm optimization algorithm will fall into the local best situation under shading,the algorithm search process is improved,and the shrinking particle swarm optimization algorithm is proposed.Establish a repeated shrinkage mechanism and an oscillation shrinkagemechanism to shrink the particles that have repeatedly searched for areas that have been searched for by other particles and that have fallen into a local peak point oscillation search to reduce the number of particle populations.Reduce the number of algorithm iterations,avoid repeated search,accurately track to the maximum power point and shorten the algorithm convergence time.(4)Improvement of algorithm inertia weight.By adding the inertia adjustment parameter?,the algorithm is searched with a larger inertia weight w value in the early stage,improving the algorithm's global search ability to avoid falling into the local best point.In the later stage of the algorithm,the smaller w value is used to search the space more carefully,which is convenient for accurate to find the maximum power point.Set the restart condition of the algorithm.When the light intensity changes,restart the algorithm to track the maximum power point.(5)Simulation verification of shrinking particle swarm algorithm.By building a simulation model,the algorithm is simulated and verified under static and dynamic conditions.The output results prove that the algorithm can effectively reduce the number of particle searches and shorten the convergence time of the algorithm,and can accurately track the maximum power point when the photovoltaic power generation system is shaded.