Research On Maximum Power Point Tracking Of Photovoltaic Power Generation Under Partial Shading

With the continuous development of society and technology,the human demand for energy is also increasing.Since the burning of fossil energy can cause problems such as the greenhouse effect and environmental pollution,renewable energy has received much attention.Solar energy is safe,environmentally friendly and energy-rich,making it of the most popular renewable energy sources in recent years.Photovoltaic(PV)power generation is a technology that uses semiconductor materials to convert solar energy into electrical energy.In the practical PV power generation process,due to factors such as cloud cover or bird activity,PV arrays are subject to Partial Shading Conditions(PSC),which leads to power mismatch,reduced efficiency of PV system power generation,and influence the reliability of PV systems.To improve the power generation efficiency of PV power systems under partial shading,the main research is as follows.Firstly,the simulation model of the 5×5 PV array is established,and analyze the PV output characteristics of PV cells are under different operating conditions.The differences in shading area,shading degree,and shading method for partial shading under PSC are focused on the degree of impact on PV characteristic curves and power mismatch to evaluate the power generation output performance under different operating conditions.Secondly,the algorithms used for domestic and foreign Maximum Power Point Tracking(MPPT)techniques are analyzed and discussed.and the Cuckoo Search Algorithm(CSA)is selected as the Maximum Power Point Tracking control algorithm because of its versatility,few control parameters,and fast algorithm execution.The CSA algorithm is compared with the Particle Swarm Optimization(PSO)and Gravitational Search Algorithm(GSA)in simulation.It is concluded that the MPPT of CSA,PSO,and GSA tend to fall into the Local Maximum Power Point(LMPP)under the local shading and thus cannot track the Global Maximum Power Point(GMPP).To address the above problems,a Cuckoo Search Algorithm based on the Gravitational Acceleration Mechanism,(CSAGAM)MPPT method is proposed based on the improvement of the Cuckoo Search Algorithm.The Cuckoo Search Algorithm has an excellent global search capability,but in the late iteration of the algorithm,it tends to oscillate back and forth between multiple feasible solutions,making it difficult to accurately track down the unique optimal solution.The solution is to take advantage of its ability to perceive global optimal information without learning changes in external environmental factors by introducing a universal gravitational search mechanism.Equating cuckoo nests to different masses of individuals.It makes CSAGAM follow the Levy flight law and the law of gravity in the process of finding the best,which enhances the search power of the CSA in the late iteration and reduces the number of oscillations.Meanwhile,based on the CSA,a probabilistic mutation method is proposed to increase the population diversity and avoid falling into local optimal solutions.CSAGAM effectively improves global search and local identification capabilities,enabling PV systems to track MPPs faster and more accurately.Finally,compared with the CSA,CSAGAM algorithm improves the tracking speed by more than 65%,and compared with the popular PSO.The GSA is simulated and compared.The results show that the proposed algorithm has a faster convergence speed,higher tracking efficiency,and smaller power fluctuation in photovoltaic MPPT,which improves the efficiency and power quality of photovoltaic power generation.