The Performance Improvement of MMC-DFIM Drives using Hybrid Jellyfish-Butterfly Optimized FOPID Control

The development of advanced wind energy conversion systems (WECS) necessitates efficient converter control strategies to enhance energy conversion efficiency and minimize harmonic distortion.This study proposes a novel hybrid optimization framework that combines Jellyfish Search (JS) and Butterfly Optimization Algorithm (BOA) to adaptively tune Fractional Order Proportional-Integral-Derivative (FOPID) controllers for Modular Multilevel Converters (MMCs) interfaced with Doubly-Fed Induction Machines (DFIMs).The hybrid JS-BOA approach leverages JSs global convergence capabilities and BOAs superior exploration behavior, overcoming the performance constraints inherent in single-algorithm methods.The control strategy integrates a real-time FOPID controller with 3D Space Vector Pulse Width Modulation (3D-SVPWM) and predictive capacitor voltage sorting to suppress circulating current and maintain precise capacitor voltage balancing.MATLAB/Simulink R2024a simulations, alongside experimental validation using a 7-level MMC testbed equipped with a dSPACE 1202 controller, were conducted to verify system performance.Comparative analyses demonstrate that the proposed JS-BOAbased FOPID controller outperforms traditional optimization techniques such as Particle Swarm Optimization (PSO), Monarch Butterfly Optimization (MAO), and Multi-Directional Hunger Optimization (MDHO).Notably, it achieves a 30-45% improvement in circulating current suppression, maintains 2 V precision in voltage balancing, and reduces settling time to approximately 90 ms under varying wind profile conditions.This research establishes a scalable and computationally efficient control framework that ensures low Total Harmonic Distortion (THD) and compliance with regulatory standards, contributing significantly to the advancement of next-generation wind energy systems.