Fast Artificial Neural Network Based Method for Estimation of the Global Maximum Power Point in Photovoltaic Systems

In partially shaded photovoltaic (PV) arrays, the power-voltage curve displays multiple maxima, one of which is the global maximum power point (GMPP). One of the major concerns related to PV systems is to locate and track the GMPP in all circumstances to boost efficiency. Combining conventional hill climbing (HC) algorithm and artificial neural networks (ANNs), a new two-stage GMPP tracking method is introduced in this article that aims to be fast and accurate. Moreover, it does not require irradiance or temperature sensors. In the first stage, the current-voltage (I-V) curve is sampled at specific points determined based on the array I-V curve analysis with the objective to be the minimum samples possible that can reflect the changes of irradiance and temperature. Then a simple feedforward ANN is employed to estimate the neighborhood of the GMPP using these samples. In the second stage, the HC algorithm is adopted to ensure the GMPP is tracked precisely. The proposed method is validated by simulations in MATLAB/Simulink environment and experimental tests under uniform irradiance condition, partial shading conditions, and a wide range of temperatures.