Neural Network (NN), Perturb and Observe (PO), and Hybrid NN-PO for MPPT Controller in PV System

• Published in: 2024 IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INTELLIGENT SYSTEMS, I2CACIS 2024
• Main Authors: Fikry, Ahmad Saufi Bin Lokman; Saaidin, Shuria; Sulaiman, Norakmar; Kutty, Suhaili Beeran; Kassim, Murizah
• Format: Proceedings Paper
• Language: English
• Published: IEEE 2024
• Subjects: Automation & Control Systems; Computer Science
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-recordWOS:001308267400075

The need for renewable energy in power systems is growing exponentially. Several algorithms may be used to track the Maximum Power Point (MPP) quickly and precisely. This research provides a comparison and analysis of different control techniques for the maximum power point tracking (MPPT) of a photovoltaic system subject to varying irradiance and temperature by using three algorithms which are Perturb and Observe (PO), Artificial Neural Network (ANN), and Hybrid NN-PO. The three MPPT algorithms were created in a standalone photovoltaic system with a boost converter to maintain the maximum power point of the solar panel. Using MATLAB/SIMULINK software, the performance of these controllers is evaluated under varying irradiance and temperature conditions. Under the 100 (W/m2s) slope, PO's efficiency is the lowest, at 96.443% and the hybrid efficiency is nearly identical to the ANN algorithm at 99,996% and 99,997%, respectively. Based on the simulation that has been demonstrated, the Perturb and Observe (PO) algorithm exhibits the lowest performance in the simulation with time response. The Hybrid Neural Network and Neural Network algorithm performs better than PO. At the same time, hybrid efficiency is similar to the ANN algorithm.