Disturbance observer-based finite-time adaptive neural control scheme of DFIG-wind turbine

被引：0

作者：

Bounar, Naamane ^{[1
]}

Boulkroune, Abdesselem ^{[1
]}

Labdai, Sami ^{[2
]}

Chrifi-Alaoui, Larbi ^{[2
]}

Khebbache, Hicham ^{[1
]}

机构：

[1] Univ Jijel, LAJ Lab, BP 98, Jijel, Algeria

[2] Univ Picardie Jules Verne, Lab Innovat Technol LTI, UR UPJV 3899, Amiens, France

来源：

WIND ENGINEERING | 2025年 / 49卷 / 02期

关键词：

DFIG; wind turbine; neural disturbance observer; finite-time convergence; adaptive control; neural networks; FED INDUCTION GENERATOR; TRACKING CONTROL;

D O I：

10.1177/0309524X241263517

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper introduces a novel disturbance observer-based finite-time adaptive neural control approach to optimize wind power conversion in a doubly fed induction generator-based wind turbines (DFIG-WT). This control strategy offers appealing features including rapid finite-time convergence, both transient and steady-state performance enhancements, and robustness against external disturbances and inherent model uncertainties. The control strategy integrates the neural networks estimation capability with the interesting proprieties of the finite-time control method to achieve efficient wind power conversion. Closed-loop finite-time stability is conducted using the finite-time Lyapunov stability concept of nonlinear systems. The developed control strategy's effectiveness is confirmed through numerical simulation.

引用

页码：271 / 289

页数：19

共 33 条

[1] An advanced robust fault-tolerant tracking control for a doubly fed induction generator with actuator faults [J].

Abdelmaleki, Samir ;

Barazane, Linda ;

Larabi, Abdelkader .

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2017, 25 (02) :1346-+

[2] Maximum power extraction from a wind turbine using second-order fast terminal sliding mode control [J].

Abolvafaei, Mahnaz ;

Ganjefar, Soheil .

RENEWABLE ENERGY, 2019, 139 :1437-1446

[3] Speed control of a wind turbine-driven doubly fed induction generator using sliding mode technique with practical finite-time stability [J].

Ali, Mohammad ;

Amrr, Syed Muhammad ;

Khalid, Muhammad .

FRONTIERS IN ENERGY RESEARCH, 2022, 10

[4] Nonlinear predictive control of a DFIG-based wind turbine for power capture optimization [J].

Bektache, A. ;

Boukhezzar, B. .

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2018, 101 :92-102

[5] Fractional-order neural control of a DFIG supplied by a two-level PWM inverter for dual-rotor wind turbine system [J].

Benbouhenni, Habib ;

Colak, Ilhami ;

Bizon, Nicu ;

Abdelkarim, Emad .

MEASUREMENT & CONTROL, 2024, 57 (03) :301-318

[6] Observer backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation [J].

Bossoufi, Badre ;

Karim, Mohammed ;

Lagrioui, Ahmed ;

Taoussi, Mohammed ;

Derouich, Aziz .

RENEWABLE ENERGY, 2015, 81 :903-917

[7] Adaptive Fuzzy Control Scheme for Variable-Speed Wind Turbines Based on a Doubly-Fed Induction Generator [J].

Bounar, N. ;

Labdai, S. ;

Boulkroune, A. ;

Farza, M. ;

M'Saad, M. .

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING, 2020, 44 (02) :629-641

[8] Fixed-time fuzzy adaptive control scheme for doubly fed induction generator-based wind turbine [J].

Bounar, Naamane ;

Boulkroune, Abdesselem ;

Labdai, Sami ;

Chrifi-Alaoui, Larbi .

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2024, 46 (08) :1579-1589

[9] First and High Order Sliding Mode Control of a DFIG-Based Wind Turbine [J].

Djilali, Larbi ;

Sanchez, Edgar N. ;

Belkheiri, Mohammed .

ELECTRIC POWER COMPONENTS AND SYSTEMS, 2020, 48 (1-2) :105-116

[10] Neural Networks for Stable Control of Nonlinear DFIG in Wind Power Systems [J].

Douiri, Moulay Rachid ;

Essadki, Ahmed ;

Cherkaoui, Mohamed .

PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017), 2018, 127 :454-463

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