Self-Learning Fuzzy Feedforward Compensation Algorithm In Photovoltaic MPPT

The application of self-learning fuzzy feedforward compensation algorithm in photovoltaic maximum power point tracking is to compensate the voltage and current of photovoltaic system. The basic idea of this method is to generate a set of compensation coefficients, so as to construct a light intensity feedforward compensation channel. At the same time, according to the disturbance observation results, the fuzzy rule base is online learned to eliminate the influence of temperature changes on the accuracy of nonlinear approximation. It is used to adjust the voltage and current values at different times according to the actual situation measured by the sensor (such as temperature, solar irradiance, etc.). This method can be used in DC and AC applications. In order to further improve the steady-state accuracy, the variable step disturbance observation method is used to design the feedback controller. Simulation and experimental results show that the proposed method has good adaptability and robustness to light intensity mutation, and significantly improves the conversion efficiency of photovoltaic system.