Photovoltaic Multi-peak Maximum Power Point Predictive Tracking Method Based on Flat Parabolic Model

Existing photovoltaic cell models are no longer applicable under partial shading conditions. In this paper, based on the similarity between the current-voltage characteristic curve of photovoltaic arrays and the particle flat parabolic motion trajectory, a kinetic flat parabolic model for photovoltaic arrays under shaded conditions is built and is solved by the improved particle swarm algorithm to obtain the maximum power prediction. Because the flat parabolic model only fits the current-voltage characteristic curve of photovoltaic arrays, there are errors when the model is used alone to predict the maximum power point. To deal with the above problem, an index variable step conductor increment method is used to track the maximum power point after estimating it by the flat parabolic model. The multi-peak optimization method is validated by the simulation results under different operating conditions in MATLAB. This method can effectively reduce the optimization time and reduce the oscillation during optimizing process. The convergence speed and photovoltaic generation efficiency can be increased.