Research On Neural Network And Fuzzy Control Combined For Photovoltaic MPPT Control

With the rapid development of global economy, a large number of traditional fossil fuel consumption, coal, oil, natural gas and other energy reserves fell sharply, the energy crisis appeared. In this context, the solar energy as a new kind of green renewable energy was favored by all. Especially in recent years, due to the rapid economic development, the photovoltaic industry has been hitherto unknown development. However, due to the high cost of solar cell conversion efficiency is low, seriously hindered the further development of photovoltaic industry. Therefore, the method of maximum power point tracking to improve the conversion efficiency of solar cells made a lot of research. Therefore, the focus of this paper is to make solar cells work at the maximum point, so as to obtain the maximum output power.Firstly, the simulation model of MATLAB/Simulink established the BOOST conversion circuit and photovoltaic cells, photovoltaic current-voltage characteristics and performance were analyzed in detail. Secondly, this paper analyzes the current several commonly used maximum power tracking method, and gives some simple measures of improvement. Then, the BP neural network was established based on the maximum power point voltage prediction model and the simulation experiments, the experimental results showed that can predict the maximum power voltage quickly and accurately by using neural network.Finally, the common method of the existing problems puts forward the photovoltaic MPPT control method of neural network is combined with fuzzy control, and establishes the simulation model. The simulation results show that, this method can be very good to avoid power oscillation and the error in the traditional method, and the system response speed, high precision.