A new MPPT control strategy based on a weighting mechanism: Enhancing efficiency in solar energy harvesting
Laarabi El oussoul, Achour Elhamdaouy, Abdessalam Aït Madi
Abstract
• The paper proposes a new Maximum Power Point Tracking algorithm that uses a weighting mechanism. • By using this weighted approach, the algorithm enhances the tracking accuracy and efficiency of MPPT, even under varying environmental conditions. • The proposed algorithm aims to optimize the performance of the PV system by maintaining the most effective operation point despite fluctuating solar irradiance and temperature. • This innovative approach can be applied in a wide range of photovoltaic systems, contributing to more stable and efficient solar energy harvesting. • The weighting mechanism dynamically adjusts to the changing power and voltage characteristics, offering an adaptive response to real-time conditions. • From a technical standpoint, the proposed technique is straightforward, computationally efficient, and well-suited for implementation on low-cost microcontrollers, making it a practical solution for photovoltaic systems. Traditional Maximum Power Point Tracking (MPPT) algorithms like Perturb and Observe (P&O) and Incremental Conductance (IC) perform well under stable conditions but struggle with rapid changes in solar irradiation or partial shading, often getting stuck at local maxima. Advanced algorithms, such as metaheuristic-based or fuzzy logic approaches, handle these challenges but may cause oscillations in fluctuating environments and require high processing power, posing a challenge for resource-limited hardware. This paper presents a new MPPT algorithm that incorporates a weighting mechanism. In the first phase, the power curve slope and voltage variation of the PV generator are scaled and converted into binary format, and the step size is determined based on the bit weights. In the second phase, the step size is dynamically adapted according to the significance of the voltage variation. Weighting power slope and voltage variation enhances adaptability, enabling real-time MPP tracking, improving robustness against shading and irradiation changes, and achieving faster convergence to the GMPP with reduced oscillations. Simulations using MATLAB/Simulink were performed with a 282.72 W PV generator, consisting of four Xunlight XRU10–71 thin-film modules, and an MPP voltage of 33.34 V. Under standard test conditions (STC), the proposed MPPT algorithm achieved a tracking time of 8 ms with 99.98 % efficiency, outperforming both the P&O and fuzzy logic control (FLC) algorithms, while reducing steady-state power oscillations by >58 % compared to the nearest algorithm, FLC. The results of the simulations show highly promising outcomes; however, tests in real-world conditions will be conducted later to fully assess their potential.