Enhanced Perturb and Observe MPPT Algorithm Using Ultra-Local Model Control for Improved Photovoltaic System Efficiency

In this paper, we present an enhanced Maximum Power Point Tracking (MPPT) algorithm for photovoltaic (PV) systems, combining the well-established Perturb and Observe (P&O) method with an Ultra-Local Model (ULM) control approach. The proposed technique aims to improve the efficiency and performance of PV systems by addressing the limitations of the traditional P&O algorithm, such as slow response times and oscillations around the maximum power point (MPP) under varying environmental conditions. The ULM-based MPPT algorithm dynamically adjusts the reference voltage used in the P&O method, leveraging online estimations to optimize the control action. This integration enables faster convergence to the MPP and reduces steady-state oscillations, leading to more efficient energy harvesting from the PV system. Simulations results demonstrate the superiority of the proposed method over the conventional P&O algorithm. The enhanced algorithm exhibits improved tracking accuracy, stability, and adaptability to changes in irradiance and temperature, ultimately boosting the overall efficiency of the PV system. This study highlights the potential of combining classical MPPT techniques with advanced control strategies to achieve higher performance in renewable energy systems, paving the way for more reliable and efficient solar power generation.