Hybrid Kalman Filter-Based MPPT Design for Photovoltaic System in Energy Harvesting Optimization

Abstract

A novel hybrid Maximum Power Point Tracking (MPPT) strategy is introduced in this study. By integrating Kalman filter MPPT with the grey wolf optimization (GWO) algorithm, a novel hybrid MPPT method is developed. By applying this technology to the photovoltaic system to be used for energy harvesting optimization, it is possible to simultaneously enhance the system's power quality and efficiency. TheMATLAB software is utilized to simulate the proposed method, and afterward, the obtained results are subsequently compared to the most recent advancements in MPPT methods across a variety of environmental conditions. The suggested method is tested under uniform irradiance, step changing in irradiances, and the partial shading effect to showthe performance of the PVarray. Here, the irradiance and temperature data can be received via different sources and from applications such as ThingSpeak which is an example of using the means of Internet of Things (IoT) in energy harvesting optimization. Furthermore, a comparison is made between the acquired outcomes and the most recent MPPT techniques, namely perturb and observe (P&O), Kalman filter (KF), and grey wolf optimization (GWO). The proposed methodology shows better efficiency, reduced power oscillation, and higher speed in comparison to conventional approaches.