The Research Of Photovoltaic Generation MPPT Method Based On Hybrid Optimization Neural Network Algorithm

With China's economic development, energy consumption has become increasingly serious pollution crisis, in order to ease this trend, the application of new energy has become the focus of research and development in the world. Among them, the solar energy is the most popular because of its good cleaning, safety, renewable and inexhaustible. At present, the conversion form of photovoltaic power generation is the conversion of solar energy into electricity, but the low efficiency and high cost of photovoltaic power generation limits the popularization and application of photovoltaic system in life and production. Improving the efficiency of maximum power point tracking is one of the most effective ways to improve the efficiency of photovoltaic power generation. In this thesis, the intelligent control algorithm is introduced to the maximum power point tracking, and the optimization of the BP neural network is used to optimize the optimization and prediction ability, and then combined with the constant voltage control method to achieve the maximum power point tracking of photovoltaic power generation system. The main work of this paper is as follows:Firstly, we have analyzed the current status and development trend of photovoltaic power generation, at the same time, the application prospect of neural network and its application in MPPT are described. Then, the generating principle of photovoltaic cells is simply introduced and the output characteristic curves of photovoltaic cells are obtained by simulation. The results show that the output power of the battery is affected by two main factors which are light intensity and outside temperature.Secondly, the maximum power point tracking method for solar photovoltaic array is studied. The existing MPPT methods, such as the perturbation method, the incremental conductance method, the parallel power compensation method and the Fibonacci sequence search method are analyzed, and the advantages and disadvantages are compared. MPPT based on BP neural network is studied, and the structure of BP neural network is designed and built. The simulation results show that the error of the traditional method in the maximum power point tracking is large.In addition, the improved method of MPPT is studied, and the prediction error of photovoltaic power generation(MPPT) based on BP neural network is studied. Based on the particle swarm optimization(PSO) and artificial fish swarm algorithm(AFSA), an improved BP algorithm based on artificial fish swarm optimization is proposed. According to outstanding global optimization ability, it solved the problem that the particle swarm algorithm is prone to "aggregation" in the search process, and the algorithm cannot escape the localvalue.Finially, the application method of BP neural network in MPPT is studied, and a new MPPT method based on BP neural network which is optimized by hybrid algorithm is proposed. Simulation results show that the new algorithm can effectively avoid the local extremum and has good stability and tracking accuracy in the maximum power tracking compared with the traditional method in Matlab/Simulink environment.