A novel temperature parametric method for rapid maximum power point detection in photovoltaic modules

Abstract

Photovoltaic systems (PVS) exhibit variability in their maximum power point (MPP) output due to variations in irradiance and cell temperature. This can lead to reduced efficiency, as maximum power point tracking (MPPT) algorithms often have slow response times and limited ability to adapt to rapidly changing environmental conditions. New algorithms are therefore needed to capture more energy and improve the efficiency of these systems. In this context, this article presents a new method for temperature parametric (TP) and its implementation using a digital PI controller, a buck converter, and MATLAB-Simulink. This innovative approach relies on detecting the MPP by continuously measuring the cell temperature of the PV panel (𝑇_{𝑐𝑒𝑙𝑙}) and solar irradiance (S). A 3D linear regression model connects these two parameters with the maximum current (𝐼_𝑚𝑝𝑝), enabling real-time monitoring of the MPP. We have applied this new method on two different types of PV (POLY-40W and BPSX330J) under a range of environmental conditions, including stable and dynamic scenarios. The results of the simulation demonstrate the superiority of our approach compared to the hill climbing (HC) for perturbation steps of HC (1%) and HC (2%). Our method achieves faster convergence time 0.009 s and high MPPT efficiency at 98.18%, fewer steady-state oscillations, and better detection.