Enhancing Power Tracking Efficiency in Stand-Alone PV Systems via ‎Adaptive Perturb and Observe (P&O)‎‏ ‏Optimization ‎

Abstract

Enhancing efficiency in photovoltaic (PV) power production is a significant engineering challenge, particularly under varying weather and climatic conditions. PV systems offer a green and inexhaustible source of energy; actually, their performance is highly affected by climate variables like as solar sunlight, cell temperature, and partially cloudy conditions. The Maximum Power Point Tracking (MPPT) algorithm commonly utilizes Perturb and Observe (P&O) technique. However, when a large perturbation step is utilized, the algorithm method may oscillate around the peak maximum power point. Conversely, using a small step size enhances tracking accuracy but significantly slows the response time, limiting the system's ability to promptly reach the true MPP under rapidly changing environmental conditions. For solving these issues, a better approach is proposed that uses adaptive step sizes within. This improved algorithm dynamically adjusts the duty cycle of the boost converter's, allowing for more efficient and precise tracking of the MPP with changing climate conditions. A microcontroller is required for the hardware implementation of MPPT. This microcontroller is a component of a robust circuit, namely a solar charge controller. The off-grid PV electricity system is simulation based on MATLAB/Simulink R2024a. The adapted P&O algorithm produces a smoother output and achieves higher efficiency than the traditional fixed-step P&O.