Improved Performance of a PV Solar Panel with Adaptive Neuro Fuzzy Inference System ANFIS based MPPT

• Main Authors: Amara, Karima, Fekik, Arezki, Hocine, D., Bakir, Mohamed Lamine, Bourennane, El-Bay, Malek, Toufik Ali, Malek, Ali
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptive Neuro Fuzzy Inference System (ANFIS); Adaptive systems; Artificial neural networks; Fuzzy logic; Matlab/Simulink; Maximum power point trackers; Maximum Power Point Tracking (MPPT); Perturb and Observe (P&O); Photovoltaic (PV) system; Photovoltaic systems; Voltage control
• ISSN: 2572-6013
• DOI: 10.1109/ICRERA.2018.8566818

This article presents the development of an intelligent technique of Adaptive-Neuro-Fuzzy Inference System (ANFIS) based on Maximum Power Point Tracking (ANFIS-MPPT) algorithm with PI controller in order to increase the performances of the photovoltaic panel system below change atmospheric circumstances. In this work, the mathematical principles of the ANFIS method were presented and developed using the software Matlab/Simulink. Moreover, the effectiveness of this ANFIS-MPPT technique is demonstrated by a comparison of the obtained results with others obtained from a classical (Perturb & Observe) P & O-MPPT method.From the analysis of the obtained results, the ANFIS-MPPT command provide better performances, respectable dynamic operations, quicker convergence and fewer fluctuations of working point nearby MPP compared to the classical P & O-MPPT under every irradiance conditions. We have confirmed, here, the capacity of the ANFIS-MPPT technique to increase not only the performance but also the tracking accuracy, speed and system stability under varying climatic conditions. Such improvement would be very beneficial when looking at the significance of the network coupled PV system.