Cuckoo Search Algorithm and Artificial Neural Network-based MPPT: A Comparative Analysis

• Published in: 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) pp. 1 - 5
• Main Authors: Farooqui, Shoeb Azam, Khan, Rashid Ahmed, Islam, Noorul, Ahmed, Naeem
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.11.2021
• Subjects: Artificial Neural Network (ANN); Artificial neural networks; Atmospheric modeling; Cuckoo search algorithm (CSA); Maximum power point trackers; Maximum power point tracking (MPPT); Photovoltaic (PV) module; Steady-state; Temperature; Temperature dependence; Training
• ISSN: 2687-7767
• DOI: 10.1109/UPCON52273.2021.9667651

Solar PV system has exponentially gained its popularity and applications in residential, industrial, battery charging, and solar-powered vehicles. Because of its certain shortcomings such as low conversion efficiency, non-linear characteristic, high cost of installation, and dependability on environmental conditions that is ambient temperature and solar radiation, it drew the researcher's attention to overcome these limitations. Numerous MPPT (maximum power point tracking) algorithms have been presented in this regard in order to capture the maximum power from a solar PV module. Among these MPPT techniques, only a few can effectively work under varying atmospheric conditions. In this article, artificial neural network-based MPPT is presented and cuckoo search algorithm-based MPPT has been used and their comparative analysis has been done. The simulation is performed and the outcome is then validated using MATLAB/Simulink.