Enhanced MPPT method based on ANN-assisted sequential Monte-Carlo and quickest change detection

The performance of a photovoltaic system is subject to varying environmental conditions, and it becomes more challenging to track the maximum power point (MPP) and maintain the optimal performance when partial shading occurs. In this study, an enhanced MPP tracking (MPPT) method is proposed utilising the state estimation by the sequential Monte-Carlo (SMC) filtering, which is assisted by the prediction of MPP via an artificial neural network (ANN). A state-space model for the sequential estimation of MPP is proposed in the framework of incremental conductance MPPT approach, and the ANN model based on the observed voltage and current or irradiance data predicts the global MPP to refine the estimation by SMC. Moreover, a quick irradiance change detection method is applied, such that the SMC-based MPPT method resorts to the assistance from ANN only when partial shading is detected. Simulation results show that the proposed enhanced MPPT method achieves high efficiency and is robust to rapid irradiance change.