MPPT Simulation Research Of Photovoltaic System Based On Improved Butterfly Optimization Algorithm

Photovoltaic power generation occupies a pivotal position in the field of smart grid and new energy.It is one of the key technologies to realize energy structure transformation and address climate change,and is conducive to building a diversified and complementary energy system.The power generation efficiency of photovoltaic power generation is greatly affected by external environmental factors,and its output characteristic curve changes significantly with changes in external conditions.Maximum Power Point Tracking(MPPT)is a key technology to improve the power generation efficiency of photovoltaic power generation systems However,the traditional maximum power point tracking algorithm is difficult to guarantee its tracking accuracy in the face of complex and changeable environmental conditions,and it is easy to fall into local optimum,which makes the system unable to achieve the maximum power output.It is of great research significance to quickly and accurately track the maximum power point of the photovoltaic system in an environment to further improve the efficiency and reliability of photovoltaic power generation.The research work of this paper is as follows:(1)According to the working principle of the photovoltaic cell and its mathematical model,build a photovoltaic cell simulation model,analyze the influence of different external conditions such as light intensity and temperature on the output characteristics of the photovoltaic cell,and further build a photovoltaic array simulation model on this basis,set the light The output characteristics of photovoltaic arrays are studied under four working conditions with different intensities.The simulation results show that under complex illumination patterns,the output characteristics of photovoltaic arrays present obvious multi-peak and multi-knee characteristics.(2)Build a simulation model through MATLAB/Simulink to test the performance of various algorithms MPPT,and test the local shading mode of the traditional algorithm perturbation and observation method and conductance incremental method.The results show that the traditional algorithm is easy to fall into local Optimum is difficult to track to the maximum power point.The classic swarm intelligence algorithm-particle swarm intelligence algorithm is simulated and tested in uniform illumination and local shading mode.The results show that the particle swarm algorithm can jump out of the local optimum,but there is still room for improvement in tracking time and tracking accuracy.(3)In view of the problems existing in the above algorithms in MPPT,this paper chooses to apply the butterfly optimization algorithm to MPPT control,researches the principle,design process and mathematical model of the butterfly optimization algorithm,builds its simulation model for multi-mode simulation verification,and the results It shows that the butterfly optimization algorithm has certain advantages over the above-mentioned algorithms in terms of tracking time and tracking accuracy.(4)Further study the main influencing factors of the butterfly optimization algorithm.In order to improve the convergence speed of the algorithm and the ability to jump out of the local optimum,the nonlinear switching probability,the balanced state pool strategy in the balance optimizer algorithm,and the random walk strategy are introduced.A variety of strategies are used to improve the butterfly optimization algorithm,and 12 standard test functions are used to test the performance of the improved butterfly optimization algorithm.The simulation results show that the improved butterfly optimization algorithm has certain advantages in terms of convergence and stability.The MPPT simulation model based on the improved butterfly optimization algorithm was built,and compared with the particle swarm optimization algorithm and the butterfly optimization algorithm for various illumination mode simulations.The simulation results show that the improved butterfly optimization algorithm can search for the global maximum power point more quickly and accurately,and It has strong adaptability and robustness in dynamic environment.