Sensor fault-tolerant control for a doubly fed induction generator in a smart grid

被引：6

作者：

Conchas, Robin F. ^{[1
]}

Sanchez, Edgar N. ^{[1
]}

Ricalde, Luis J. ^{[2
]}

Alvarez, Jesus G. ^{[3
]}

Alanis, Alma Y. ^{[3
]}

机构：

[1] CINVESTAV, Elect Engn Dept, Un Guadalajara, Ave Bosque 1145, Zapopan 45017, Jalisco, Mexico

[2] Univ Autonoma Yucatan, Fac Ingn, Ave Ind contaminantes,POB 115, Merida, Yucatan, Mexico

[3] Univ Guadalajara, Innovat Based Informat & Knowledge Dept, CUCEI, Marcelino Garcia Barragan 1421, Guadalajara 44430, Jalisco, Mexico

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2023年 / 117卷

关键词：

Fault-tolerant control; Artificial neural networks; Smart grids; Doubly fed induction generator; Power converters; SLIDING MODE CONTROL; WIND; POWER;

D O I：

10.1016/j.engappai.2022.105527

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study proposes a sensor fault-tolerant controller for a doubly fed induction generator connected to a smart grid. Mathematical models are the main tools for the synthesis of modern control systems; however, an accurate model for complex systems is not always available. Therefore, in this study, a recurrent high-order neural network trained with an extended Kalman filter is proposed to develop a mathematical model for a wind turbine with a doubly fed induction generator connected to a smart grid. The neural model is combined with a modified discrete-time sliding mode controller, which compensates for the presence of sensor faults on each of the state variables on both sides of the back-to-back converter. Real-time results are included to illustrate the effectiveness of the proposed scheme under five sensor faults.

引用

页数：12

共 20 条

[1] Sliding Mode Control Strategy for Wind Turbine Power Maximization [J].

Barambones, Oscar .

ENERGIES, 2012, 5 (07) :2310-2330

[2] Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement [J].

Benbouzid, Mohamed ;

Beltran, Brice ;

Amirat, Yassine ;

Yao, Gang ;

Han, Jingang ;

Mangel, Herve .

ISA TRANSACTIONS, 2014, 53 (03) :827-833

[3] The history and state of the art of variable-speed wind turbine technology [J].

Carlin, PW ;

Laxson, AS ;

Muljadi, EB .

WIND ENERGY, 2003, 6 (02) :129-159

[4] Discrete-Time Neural Sliding-Mode Block Control for a DC Motor With Controlled Flux [J].

Castaneda, Carlos E. ;

Loukianov, Alexander G. ;

Sanchez, Edgar N. ;

Castillo-Toledo, Bernardino .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2012, 59 (02) :1194-1207

[5] Real-time Neural Inverse Optimal Control for Low-Voltage Rid-Through enhancement of Double Fed Induction Generator based Wind Turbines [J].

Djilali, Larbi ;

Sanchez, Edgar N. ;

Ornelas-Tellez, Fernando ;

Ruz-Hernandez, Jose A. ;

Ricalde, Luis J. .

ISA TRANSACTIONS, 2021, 113 :111-126

[6] Real-time implementation of sliding-mode field-oriented control for a DFIG-based wind turbine [J].

Djilali, Larbi ;

Sanchez, Edgar N. ;

Belkheiri, Mohammed .

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2018, 28 (05)

[7]

Hamayun MT, 2016, STUD SYST DECIS CONT, V61, P1, DOI 10.1007/978-3-319-32238-4

[8]

La Seta P, 2007, NEW CONTROL SCHEME D

[9] Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties [J].

Liang, Hongjing ;

Liu, Guangliang ;

Zhang, Huaguang ;

Huang, Tingwen .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (05) :2239-2250

[10] Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation [J].

Pena, R ;

Clare, JC ;

Asher, GM .

IEE PROCEEDINGS-ELECTRIC POWER APPLICATIONS, 1996, 143 (03) :231-241

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