Hybrid beluga whale optimization based MPPT for photovoltaic powered open end winding induction motor drives

This paper presents a novel Hybrid Beluga Whale Optimization (HBWO) algorithm enhanced with multi-strategy mechanisms for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems integrated with an Open-End Winding Induction Motor (OEWIM) drive. The HBWO algorithm is developed to ensure fast and accurate maximum power extraction under dynamic irradiance, partial shading, and varying load conditions. The motor drive is governed by an improved Direct Torque Control (DTC) scheme featuring a five-level torque hysteresis controller and an optimized switching table, resulting in reduced torque ripple and enhanced system stability. Simulation-based performance analysis, conducted in MATLAB/SIMULINK, benchmarks the proposed method against traditional MPPT algorithms including Perturb & Observe (P&O), Particle Swarm Optimization (PSO), Chimp Optimization Algorithm (COA), and Giza Pyramid Construction (GPC). Quantitative results demonstrate that HBWO achieves a DC link voltage rise time of 0.11 s, overshoot of 0.382%, settling time of 0.17 s, steady-state ripple of 0.50%, and steady-state error of only 0.04%. In terms of motor speed control, the system delivers a rise time of 0.1 s, overshoot of 0.37%, settling time of 0.18 s, and steady-state error of 0.03%. Furthermore, the torque ripple is minimized to just 0.5%, significantly outperforming conventional algorithms such as P&O (5.6%) and COA (2.2%). These improvements confirm the HBWO algorithm's superiority in terms of convergence speed, control accuracy, and system robustness. The integration of advanced hybrid optimization with refined motor control architecture offers a comprehensive and efficient solution for renewable energy systems. This work contributes to the development of intelligent PV-driven motor systems capable of maintaining high performance and stability under real-world, variable conditions.