Photovoltaic maximum power point tracking based on BP neural network

As the energy crisis becomes more and more serious, the world pays more and more attention to new energy. Photovoltaic has become one of the most potentially valuable forms of new energy because of its inexhaustible and pollution-free advantages, but photovoltaic is inefficient at present. In order to make full use of solar energy and improve the efficiency of solar energy utilization, photovoltaic maximum power point tracking (MPPT) technology has become a research hotspot. A maximum power tracking method based on BP neural network is proposed in this paper to solve the contradiction between the traditional MPPT method in steady-state performance and dynamic performance. Photovoltaic MPPT tracking system of this paper is mainly composed of photovoltaic array, Buck circuit, BP neural network, PWM signal circuit and load. Firstly, the light intensity and temperature of the photovoltaic array are measured, then the light intensity and temperature information are transmitted into BP neural network to identify the maximum output power (Pm) and output voltage (Um) corresponding to the Pm under the current conditions. The PWM signal circuit generates pulse width modulation (PWM) control signal according to the Pm and Um, finally the thyristor of Buck circuit is controlled by the PWM signal to track the maximum power. Finally, using matlab/simulink build a simulation model to verify the efficiency of the algorithm, the simulation and experimental results show that when the light intensity and temperature of the photovoltaic array change, the system can quickly track and output the maximum power under the current conditions. The system has the following advantages: quick response, small delay, small output fluctuation, good stability.