Artificial intelligent-based backstepping control of parallel-connected multi-machine wind generation system using a new fifteen-switch rectifier

• Published in: Wind engineering 2024-10
• Main Authors: Sakouchi, Abderrahim, Bounadja, Elhadj, Djahbar, Abdelkader, Moualdia, Abdelhafidh
• Format: Article
• Language: English
• ISSN: 0309-524X; 2048-402X
• DOI: 10.1177/0309524X241270424

This academic work offers a new variable-speed wind power generation system (NVS-WPGS) that employs two 5-phase permanent magnet synchronous generators (PMSGs) controlled through a fifteen-switch rectifier (FSR) architecture for a system that’s integrated to the power grid. The two 5-phase PMSGs are linked to the DC bus through an FSR, which, in turn, connects the DC link to the grid via a three-phase inverter. To ameliorate the operational efficiency of the wind system under investigation, the backstepping control (BSC) approach was employed. Building upon this technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was trained and implemented for controlling the wind system. The goal is to boost system performance and control. While BSC is a strong control method for reference signal tracking, its dependence on system parameters is a downside. This dependence typically degrades performance. On the other hand, ANFIS exhibits robustness to variations in system parameters and uncertainties. The efficaciousness of the suggested control methodology was evaluated via experimental investigations conducted using the Matlab/Simulink program. The simulations were performed to verify the system’s capacity to maintain controlled variables in accordance with desired references, even in the presence of fluctuations in wind velocity. Simulations exhibit that the suggested ANFIS-BSC control system accurately tracks controlled intended references. Furthermore, system performance improved. Speed overshoot was decreased by 100%. Additionally, the system’s efficiency was enhanced, reaching a level of 96.5%, beating the BSC approach’s 96%. These findings underscore the remarkable effectiveness of the ANFIS-BSC approach compared to BSC methodologies.