| Photovoltaic power generation has the characteristics of instability and dispersion,resulting in low efficiency and high construction costs.Therefore,improving power generation efficiency is a key technical issue that must be addressed in the vigorous development of photovoltaic power generation.Maximum Power Point Tracking(MPPT)technology is one of the research hotspots in photovoltaic power generation technology.Traditional MPPT algorithms can only find the maximum value under uniform illumination.When the photovoltaic cell is partially occluded,its output power and output voltage characteristic curve will exhibit multi peak output characteristics.At this time,the traditional MPPT algorithm is no longer applicable and is easily trapped in a local optimal state.Therefore,conducting research on the MPPT method in photovoltaic power generation systems under local shading has significant practical significance in improving the power generation efficiency of photovoltaic power generation.(1)The engineering mathematical model of photovoltaic cells was established,and the equivalent circuit was drawn.The output characteristics of photovoltaic cells under partial occlusion were analyzed through experimental research.The emphasis is placed on the mathematical engineering modeling and equivalent circuit simplification of photovoltaic cells under partial occlusion.An experimental model under partial occlusion is designed,and then its P-U output characteristics are analyzed through simulation data,laying a good theoretical foundation for the next step of improving the MPPT algorithm for partially occluded photovoltaic arrays.(2)Aiming at the MPPT algorithm in photovoltaic power generation systems,based on the basic principle of MPPT,the boost circuit and the buck circuit are analyzed in detail,and the relevant formulas are derived;Four common MPPT algorithms,including conductance increment method,disturbance observation method,constant voltage method,and fuzzy control algorithm,are analyzed,and their shortcomings in partially occluded environments are obtained;At the same time,the solutions of intelligent algorithms such as ant colony optimization algorithm and neural network genetic optimization algorithm in local shading multi-peak MPPT are summarized,providing a theoretical basis for the next step of quantum particle swarm optimization research.(3)In response to the local optimality and instability of particles in the application of quantum particle swarm optimization in MPPT,the particle contraction expansion coefficient and position update equation of quantum particle swarm optimization were optimized based on this,and logistic functions and position update equations were introduced,respectively βThe function prevents particles from falling into the illusion of local optima.In addition,in the case of sudden changes in shading,the global optimal position will move,and the restart condition of the algorithm will be added.Finally,the optimized algorithm was tested using standard test functions,and the experimental results showed that the tracking speed and convergence speed of the optimized algorithm were better than traditional algorithms.(4)The workflow of LQPSO algorithm based on quantum particle swarm optimization is designed,and the MPPT function is implemented.The simulation models of each circuit module in the photovoltaic power generation system are built on the Matlab/Simulink simulation platform.The LQPSO algorithm is applied to the photovoltaic power generation MPPT simulation system for simulation experiments.The experimental results show that the optimized algorithm has high convergence speed and tracking accuracy,and the output power of the photovoltaic power generation system is significantly improved under dynamic and static occlusion.The effectiveness of the LQPSO algorithm is verified by a power station. |
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