Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation-Reference-Cited by-同舟云学术

Artificial neural network-based direct power control to enhance the performance of a PMSG-wind energy conversion system under real wind speed and parameter uncertainties: An experimental validation

Published:2024-06 Issue: Volume:11 Page:4356-4378
ISSN:2352-4847
Container-title:Energy Reports
language:en
Short-container-title:Energy Reports

Author:

Majout Btissam,Bossoufi Badre,Karim Mohammed,Skruch Paweł,Mobayen Saleh,El Mourabit Youness,El Zair Laggoun Zakaria

Publisher

Elsevier BV

Reference45 articles.

1. New application of artificial neural network-based direct power control for permanent magnet synchronous generator;Akkouchi;Electr. Eng. Electromechanics,2021

2. Optimal Estimation and Tracking Control for Variable-speed Wind Turbine with PMSG;Bakhtiari;J. Modern Power Syst. Clean Energy,2020

3. Experimental enhancement of fuzzy fractional order PI+I controller of grid connected variable speed wind energy conversion system;Beddar;Energy Convers. Manag.,2016

4. Wind energy conversion system based on dual stator induction generator controlled by nonlinear backstepping and pi controllers;Benakcha;Int. J. Syst. Assur. Eng. Manag.,2019

5. Direct Power Control Based on Modified Sliding Mode Controller for a Variable-Speed Multi-Rotor Wind Turbine System Using PWM Strategy;Benbouhenni;Energies,2022

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1. Adaptive passive fault tolerant control of DFIG-based wind turbine using a self-tuning fractional integral sliding mode control;Frontiers in Energy Research;2024-08-20

2. Robust Speed Control of Permanent Magnet Synchronous Motor Drive System Using Sliding-Mode Disturbance Observer-Based Variable-Gain Fractional-Order Super-Twisting Sliding-Mode Control;Fractal and Fractional;2024-06-24