Optimizing Photovoltaic System Efficiency Through a Kalman Filter Driven Approach for MPPT in Partial Shading Conditions

Abstract

An electronic tool, referred to as an MPPT (Maximum Power Point Tracker), is designed to harness the utmost power from a PV module, even amidst varying environmental conditions. However, numerous traditional MPPT techniques face challenges in accurately capturing maximum power when confronted with partial shading scenarios. Partial shading is a common challenge in PV systems, typically occurring when certain portions of interconnected series strings are shaded. This situation leads to the presence of multiple peaks in the Power-Voltage (P-V) characteristic curve of the PV system. Consequently, to pursue the maximum power under partial shading conditions, a stochastic search method called Particle Swarm Optimization (PSO) is utilized instead of traditional techniques. However, it's important to highlight that the PSO method has a drawback due to its relatively slow operation. Consequently, this research presents an innovative Kalman filter methodology pivotal for accelerating the Maximum Power Point (MPP) tracking by minimizing errors. The study re-evaluates the prerequisites of an elite boost converter with a focus on digital signal processing. Leveraging the prowess of Texas Instruments, we employ the renowned digital signal processor, TMS320F28379D. Tailored Kalman filter techniques, combined with PSO methods and specialized electronic circuits, have been conceptualized, executed, and assessed, underscoring their pivotal role in elevating the performance of the converter board. The proposed Kalman filter significantly enhances dynamic tracking efficiency, surpassing PSO by over 10% and PandO by 30%. Additionally, it markedly reduces tracking time, achieving up to a 99.998% decrease compared to traditional methods.