A Novel Improved Gradient-Based Optimizer for Single-Sensor Global MPPT of PV System

Gradient-Based Optimizer (GBO) is a highly mathematics-based metaheuristic algorithm that has garnered significant attention since its introduction. It offers several inherent advantages, such as low computational complexity, rapid convergence, and easy implementation. However, GBO has some drawbacks, including a lack of population diversity and a tendency to get trapped in local optima. To address these shortcomings, this research introduces an improved version of GBO (iGBO). In iGBO, introducing the Sobol sequence strategy ensures a higher-quality initial population and enhances the convergence speed. Additionally, a new modified Local Escaping Operator (LEO) is proposed, which incorporates the sine-cosine operator and the DCS/Xbest/Current-to-2rand strategy. This modified LEO improves the optimization efficiency of GBO and boosts its local search capability, helping to avoid local optima. The superiority of iGBO is thoroughly verified through comparisons with the original GBO and several well-known and newly developed algorithms on the IEEE CEC’2022 benchmark suite. Furthermore, the proposed approach is applied to extract the photovoltaic system’s global maximum power point (MPP) under shading conditions. Three different shading patterns are considered to assess the reliability of iGBO. The performance of the developed iGBO is compared with several leading algorithms, including Particle Swarm Optimization (PSO), Reptile Search Algorithm (RSA), Black Widow Optimization Algorithm (BWOA), Pelican OA (POA), Chimp OA (ChOA), Osprey OA (OOA), and the original GBO. The results reveal that iGBO-based MPPT consistently outperforms its competitors in identifying the global MPP under various shading conditions followed by PSO, while RSA performs the least effectively.