A Hybrid Neur-Fuzzy Optimization Method for PV Steady State Improvement in Maximum Power Point Tracking Controller - Ashty M. Aaref; Ali M. Humada (QALAAI ZANIST SCIENTIFIC JOURNAL, 2021)
This paper presents ahybrid Neural-Fuzzy optimization method for the purpose of assuringthe steady state case of the Maximum Power Point (MPP) for a Photovoatic (PV) system. Also, it works to decrease the oscillation occurring around MPP. The proposed method implemented via using an improved intelligent control method, neural fuzzy networks (Nueural-Fuzzy). The results showed that the PV system with Maximum Power Point Tracking (MPPT) always tracks the peak powerpoint of the PV module under various operating conditions. Afterwards, these results are validated by the comparison with the conventional and most used methods (P&O) and showed that the proposed MPPT method has better steady state and therefore less oscillation around the MPP than the P&O. It is also shown that, the increase in the output power due to usingthe MPPT is about 48.2% for a clear weather conditions and 34.8% for a shadow conditions