High-Efficiency MPPT for PV Systems Based on PSO-Tuned Variable Step-Size Incremental Conductance

Muhannad ALSHAREEF

Abstract

This work proposes a new method for maximum power point tracking (MPPT) for PV systems that combines particle swarm optimization (PSO) with an adaptive variable step-size (VSS) Incremental conductance (IncCond) algorithm which is able to achieve a higher tracking speed, stability and efficiency. This new step-size perturbation is dynamically determined by choosing an optimal scaling factor from a pre-trained PSO-based lookup table including only the voltage and current values. This reduces the need for computation and environmental sensors, providing a fast, accurate, and computationally efficient solution. In this context, PSO is employed for determining the appropriate scaling factor as per the available level of irradiance and so it increases the effectiveness of the VSS IncCond-based MPPT method. Extensive simulations were carried out under standard test conditions, abrupt changes in irradiance and real-world solar irradiance conditions. The obtained results confirm that the proposed method surpassed four other existing approaches, attaining an average tracking efficiency of 99.6% and an average tracking time of 0.007 seconds, and a minimal oscillation level in comparison with conventional and intelligent MPPT algorithms including fuzzy logic, ANN-based, and traditional incremental conductance methods. It has also proven to be highly adaptable and easy to implement, which is particularly important for real-time embedded PV applications in dynamic environments.

Keywords

Photovoltaic (PV) system, Maximum power point tracking (MPPT), Particle swarm optimization (PSO), Incremental Conductance (IncCond), Boost converter variable step-size (VSS).

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