A Neural Network and Fuzzy Logic based MPPT Algorithm for Photovoltaic Pumping System - Salwa Assahout; Hayat Elaissaoui; Abdelghani El Ougli; Belkassem Tidhaf; Hafida Zrouri (Institute of Advanced Engineering and Science, 2018)
The use of solar energy had gained a great attention last decades, as it is pollution-free. It is used in isolated areas for lighting, pumping, etc. However, the extraction of the maximum power generated by a PVG at any moment of the day is a big deal because the characteristic of a PVG in non-linear which makes the location of the Maximum Power Point (MPP) difficult. Therefore, a Maximum Power Point Tracking technique (MPPT) is required to maximize the output power. In this paper, a photovoltaic water pumping system has been studied. This system consists of three main parts: PVG, a DC-DC boost converter and a DC motor coupled with a centrifugal water pump. We have proposed a new MPPT algorithm based on Fuzzy logic and Artificial Neural Network (ANN) to improve the system performances. The ANN is used to predict the optimal voltage of the PVG, under different environmental conditions (temperature and solar irradiance) and the fuzzy controller is used to command the DC-DC boost converter. The proposed method is compared to P&O technic, by simulation under Matlab/Simulink, to verify its effectiveness.