Artificial Neural Network Based Maximum Power Point Tracking for PV System

• Main Authors: Ibrahim, Al-wesabi, Ding, Min, Jin, Xiaoling, Dai, Xianzhong, Sarhan, Mohammad Abu, Shafik, Muhammed Badeaa, Zhou, Hao
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial neural network (ANN); Artificial neural networks; Buck converter; Buck converters; Mathematical model; Maximum power point trackers; Maximum power point tracking (MPPT); Photovoltaic (PV); Photovoltaic systems; Radiation effects; Temperature
• ISSN: 2161-2927
• DOI: 10.23919/ChiCC.2019.8865275

Nowadays PV systems is suffering from two main problems: high production cost and low efficiency especially under variable weather conditions. Therefore, to reduce such associated problems, PV systems are being connected with various optimization controllers such as maximum power point tracking. In this paper, an artificial neural network maximum power point method has been designed to be linked between a PV system and DC-DC buck converter, and each part of the system is fully explained. The whole system was modeled under MATLAB/Simulink environment, simulation results demonstrated the efficiency of the proposed method. The proposed method has been examined to track the maximum power point under different weather conditions which shows a rapid and accurate tracking of the maximum power point (MPP) of PV system. The whole system which consists of PV arrays, Buck converter, ANN tracker, and load has been modeled using MATLAB/Simulink environment. This Simulink model has been examined using different levels of temperature and irradiation and the output results of both P-V and I-V characteristics, output power, and the system efficiency clearly demonstrate the validation of the modeled system. This demonstration has been done by comparing between the result of experimental platform and the results of simulated model which approves that ANN is highly recommended to be used in the field of maximum power point tracking of PV system.