Adaptive maximum power point tracking method for photovoltaic systems using step-size reduction and adapted Salp Swarm Optimization for dynamic conditions

Abstract

This study introduces an advanced Maximum Power Point Tracking (MPPT) framework to improve the efficiency and stability of photovoltaic (PV) systems under fluctuating irradiance and load conditions. The proposed approach integrates a Step-Size Adaptation Algorithm (SSAA) to reduce power oscillations, a deviation avoidance loop for real-time irradiance detection, and a dual-parameter perturbation algorithm (IPA/VPA) to enhance tracking speed and precision. Additionally, an Adapted Salp Swarm Optimization (ASSO) with a dynamic factor is employed to optimize boost converter performance, ensuring faster convergence and higher stability. Quantitative improvements are demonstrated through comparative analysis, highlighting superior tracking accuracy, reduced steady-state errors, and faster convergence than conventional methods such as Perturb & Observe (P&O) and Incremental Conductance (IncCond). Qualitative evaluations confirm the robustness of the framework against rapid environmental fluctuations, ensuring smooth power transitions and minimal oscillations. Simulation results validate that the combined implementation of ASSO, dual perturbation strategies, and deviation avoidance leads to a highly adaptable and efficient MPPT solution. The proposed framework outperforms existing techniques in terms of accuracy, stability, and energy extraction, making it a reliable solution for real-world PV applications.