Modelling & Analysis of GWO-Incremental MPPT Controller under Dynamic Switching Condition

PV system researchers commonly analyze P-V curves to study the multiple peaks observed under PSC. Traditional MPPT algorithms, such as P&O, HC, and IC, perform poorly in such cases, and evolutionary methods like GWO are more effective. However, GWO's inability to find the best particles globally can hinder convergence. To address these issues, a modified MGWO method is proposed that tracks data using the global best particle under both static and dynamic conditions. The MGWO improves the convergence process under static conditions and enhances re-initialization under dynamic conditions. Experiments and simulations validate the effectiveness of this approach over traditional GWO and HC algorithms. The method initializes parameters statically when a PV array is shaded dynamically, leading to optimal performance.