Optimal Energy Harvesting of Large-Scale Wind Farm Using Marine Predators Algorithm

• Published in: IEEE access Vol. 10; pp. 24995 - 25004
• Main Authors: Mohamed, Rania Gamal, Ebrahim, Mohamed Ahmed, Alaas, Zuhair Muhammed, Ahmed, M. M. R.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Controllers; Converters; Doubly fed induction generator; Doubly fed induction generators; Energy harvesting; grid side converter; Induction generators; marine predators algorithm; Maximum power point trackers; maximum power point tracking; Maximum power tracking; Optimal control; particle swarm optimization; Predators; Proportional integral; Rotors; Swarm intelligence; Transient stability; Voltage control; Wind energy; Wind farms; wind plant’s performance; Wind power; Wind turbines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3156084

A new optimal control strategy for the grid side converter (GSC) and rotor side converter (RSC) of a doubly-fed induction generator (DFIG) is developed in this paper using the Marine Predators algorithm (MPA). To accomplish this study, a comprehensive comparison between the suggested MPA-based control strategy and a well matured Particle Swarm Optimizer (PSO) to enhance transient stability of large-scale wind systems has been presented. MPA is used to determine the optimal gains of proportional-integral (PI) controllers for GSC and RSC to ensure a maximum power point tracking (MPPT) of a large-scale wind farm. The proposed optimal control strategy is analyzed and verified via a benchmark 9-MW DFIG wind farm using MATLAB/SIMULINK simulation. The attained results of the suggested MPA-PI-based controllers are compared to the conventional PI-based MPPT controllers to validate the efficacy of the developed optimal control strategy. The superiority of the proposed MPA-PI and PSO-PI-based optimal controllers over the traditional PI regulators towards enhancing the DFIG system dynamic performance has been proved. The presented MPA-PI-based control scheme has been succeeded in extracting the maximum power of the DFIG wind farm with a reduced settling time of about 1.8% and overshooting range 97% lower than the conventional controller.