A Combined Multi-Trial Vector-Based Sine Cosine Optimization and Integral Derivative Sliding Mode Control for Photovoltaic Systems Under Partial Shading Conditions

Abstract

The sine-cosine algorithm (SCA) is a metaheuristic algorithm based on the trigonometric functions sine and cosine to explore and exploit the searching area. However, typical SCA faces serious limitations, in particular, premature convergence, exploration-exploitation disequilibrium, and a susceptibility to being trapped in local maxima, making it inefficient for complex optimization challenges. To address and overcome these challenges, a multiple vector-based sine-cosine algorithm (MTV-SCA) is proposed for robust tracking of the global maximum power point (GMPP) in photovoltaic (PV) systems under partial shading conditions (PSC). The MTV-SCA integrates multiple search strategies with three adaptive control parameters through a MTV mechanism, significantly enhancing convergence speed and tracking accuracy. Furthermore, an Integral Derivative Sliding Mode Control (IDSM) is incorporated to refine the tracking process, ensuring stability and resilience against rapid environmental fluctuations. Simulation results, validated using a Boost converter, that the proposed MTV-SCA-IDSM approach achieves a fast-tracking time and high tracking accuracy across various PSC. These results confirm that the MTV-SCA-IDSM approach is a highly efficient and reliable solution for real-world PV energy harvesting applications.