Maximum power point tracking method for distributed photovoltaic energy storage output based on improved chaotic genetic algorithm

In view of the fact that the current maximum power point tracking process of distributed photovoltaic energy storage output is affected by multi peak characteristics and the tracking effect is poor, a maximum power point tracking method of distributed photovoltaic energy storage output based on improved chaotic genetic algorithm is proposed. In this method, the maximum power point tracking model of distributed photovoltaic energy storage based on Radial Basis Function (RBF) neural network is established by building an equivalent model of photovoltaic energy storage cells, analyzing the characteristics of photovoltaic energy storage cells, and taking time, light intensity and ambient temperature as inputs. Chaotic factors are introduced into the genetic algorithm to form an improved chaotic genetic algorithm. After parameter setting, population initialization, adding chaotic factors, crossover and mutation, the algorithm is used to solve the maximum power point tracking model of distributed photovoltaic energy storage based on RBF neural network, and the model is used to output the maximum power point tracking results of distributed photovoltaic energy storage output. The experimental results show that the method has good convergence and approximation when tracking the maximum power point of distributed photovoltaic energy storage output. It can effectively track the maximum power point of photovoltaic energy storage output under different ambient temperatures and light intensities, and the application effect is significant.