A Photovoltaic MPPT Method Based on Mnemonic Enhancement Optimization with the Use of Past Experience Data

The output characteristics of photovoltaic cells are easily affected by the external environment, so the study of maximum power point tracking (MPPT) technology is of great significance for improving the energy conversion efficiency of photovoltaic cells. Aiming at the problem that the traditional perturbation observation based MPPT method cannot consider the tracking speed and vibration amplitude at the same time, a mnemonic enhancement optimization (MEO) based MPPT method is proposed. This method preserves the past experience data of MPPT, and uses the experience data to estimate a good starting point for MPPT when the external environment changes. BP neural network and interpolation can be applied for the estimation of a good starting point, which can speed up the procedure of MPPT. Compared with other methods through simulations, the results demonstrate that the proposed method can significantly improve the tracking speed and adapt to severe weather.