Neural High Order Sliding Mode Control for Doubly Fed Induction Generator based Wind Turbines

Authors

Keywords:

Wind Turbine, DFIG, Neural Network, sliding control

Abstract

Wind energy has many advantages because it does not pollute and is an inexhaustible source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is developed for Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT). The stator winding is directly coupled with the main network, whereas a Back-to-Back converter is installed to connect its rotor to the grid. The proposed control scheme is composed of Recurrent High Order Neural Network (RHONN) trained with the Extended Kalman Filter (EKF), which is used to build-up the DFIG models. Based on such identifier, the High Order Sliding Mode (HOSM) using Super-Twisting (ST) algorithm is synthesized. To show the potential of the selected scheme, a comparison study considering the NHOSM, Conventional Sliding mode (CSM), and the HOSM control is done. To ensure maximum power extractions and to protect the system, the Maximum Point Power Tracking (MPPT) algorithm and the h control are also implemented. Simulation results demonstrate the effectiveness of the proposed scheme for enhancing robustness, reducing chattering, and improving quality and quantity of the generated power.

Downloads

Download data is not yet available.

Author Biographies

Larbi Djilali, Universidad Autónoma del Carmen

Djilali was born in Metlili Chaamba, Algeria in 1987. He received the B.Eng. degree in maintenance in instrumentation from university of Oran, Algeria in 2010. In 2014, he received the M. Sc. in Electrical Engineering Control Automatic from the National Polytechnic School of Oran (ENPO). In 2019, he obtained the D.Sc. degree in Electrical Engineering from university of Laghouat, Algeria, and in 2020, the D.Sc in Electrical Engineering from the National Center of Research and Advanced Studies of National Polytechnic Institute (CINVESTAV-IPN), Guadalajara campus, Mexico. Actually, he is currently a professor of electrical engineering graduate programs at the Autonomous University of Ciudad del Carmen, Campeche, Mexico. His research interests include robust control, neural control, and their applications to renewable power systems, micro-grids, power electronics converters, and electrical machines.

Anuar Badillo-Olvera, Tecnológico Nacional de México campus Zacatecas Norte

Badillo-Olvera received B. Eng. Degree in Mechatronics engineering at Polytechnic University of Zacatecas (Zac., Mexico), in 2015 the M. Eng degree in Hydraulics resources at Autonomous University of Zacatecas (Zac, Mexico) and in 2019 the D.Sc. degree in electrical enginerring at Center of Research and Advanced Studies of the IPN (Cinvestav, Jalisco, México). His research interest include Fault Diganosis in pipelines systems, optimization alghoritms, and machine learning.

Yennifer Yuliana Rios, Universidad Tecnológica de Bolívar

Yuliana Rios received the degrees of B.Sc in mechatronics engineer and M.Sc. in industrial controls from Universidad de Pamplona, Pamplona, Colombia, in 2006 and 2013, respectively, and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara Campus, Mexico, in 2019. She is currently an assistant professor at mechatronics engineering and a member of the automation and control group (GAICO) from the Universidad Tecnológica de Bolívar, Cartagena, Colombia. Her research interests include intelligent control, biomedical systems, inverse optimal control, and robotics.

Harold López-Beltrán, Centro de Investigación y Estudios Avanzados del IPN

H. Lopez-Beltran was born in Bucaramanga, Colombia in 1989. He received the B.Eng. degree in Mechatronic from University of Guadalajara, Mexico). In 2017, he studied a master of Systems Computer Science . Actually, he is currently a Research Assistant of Automatic Control in Electric Engineering graduate programs at Center for Research and Advanced Studies, National Polytechnic Institute (CINVESTAV-IPN) Guadalajara, Mexico. His research interests include complex networks, robust control, neural control, and their applications to renewable power systems, micro-grids, power electronics converters, and electrical machines

Lakhdar Saihi, Universidad de Tahri Mohammed

Saihi was born in Naama, Algeria in 1987. He received the Engineer degree in Instrumentation from Oran University in 2010 and the Magister degree in Automatic, from the Polytechnic national school (Oran, Algeria), in 2014. Currently, he is a Ph.D student in Electrical engeenring (Automatic) at University of Bechar, Algeria. He is currently Permanent Researcher at the Research Unit in Renewable Energies in the Saharan Medium (URER-MS) in Adrar Algeria. His areas research of interest are focused on advance control of Drives, electrical machines drives, process control, sensorless control of electrical machine, renewable energy systems control, wind energy, robust and nonlinear control.

References

G. Abad, J. Lopez, M. Rodriguez, L. Marroyo, and G. Iwanski,

Doubly Fed Induction Machine: Modeling and Control for Wind Energy

Generation. Hoboken, New Jersey, USA: John Wiley & Sons, 2011.

B. Beltran, M. E. Benbouzid, and T. Ahmed-Ali, “Second-order sliding

mode control of a doubly fed induction generator driven wind turbine,”

IEEE Transactions on Energy Conversion, vol. 27, pp. 261–269, June.

N. Y. Abed, M. M. Kabsha, and G. M. Abdlsalam, “Low voltage

ride-through protection techniques for dfig wind generator,” in 2013

IEEE Power & Energy Society General Meeting, (Vancouver, BC,

Canada), 21-25 July 2013.

L. Djilali, E. N. Sanchez, and M. Belkheiri, “Real-time neural sliding

mode field oriented control for a dfig-based wind turbine under balanced

and unbalanced grid conditions,” IET Renewable Power Generation,

vol. 13, pp. 618–632, 2019.

R. Pena, J. Clare, and G. Asher, “A doubly fed induction generator

using back-to-back pwm converters and its application to variable-speed

wind-energy generation,” IEE Proceedings-Electric Power Applications,

vol. 143, pp. 231–241, 1996.

F. Poitiers, T. Bouaouiche, and M. Machmoum, “Advanced control of

a doubly-fed induction generator for wind energy conversion,” Electric

Power Systems Research, vol. 79, pp. 1085–1096, July. 2009.

L. Xu and P. Cartwright, “Direct active and reactive power control of dfig

for wind energy generation,” IEEE Transactions on Energy Conversion,

vol. 21, pp. 750–758, Sept. 2006.

J. Kearney, Grid Voltage Unbalance and The Integration of DFIG.

Dublin, Ireland: Doctoral Thesis, Dublin Institute of Technology, 2013.

V. Utkin, J. Guldner, and J. Shi, Sliding mode control in

Electro-Mechanical system. Boca Raton, FL, USA: CRC press Taylor

and Francis Group, 2009.

H. Huerta, “Energy-based robust control of doubly-fed induction

generator,” Iranian Journal of Science and Technology, Transactions of

Electrical Engineering, vol. 40, pp. 23–33, May. 2016.

L. Saihi, B. Berbaoui, H. Glaoui, L. Djilali, and S. Abdeldjalil, “Robust

sliding mode h1 controller of dfig based on variable speed wind energy

conversion system,” Periodica Polytechnica Electrical Engineering and

Computer Science, vol. 64, pp. 53–63, Jan. 2020.

M. I. Martinez, G. Tapia, A. Susperregui, and H. Camblong,

“Sliding-mode control of a wind turbine-driven double-fed induction

generator under non-ideal grid voltages,” IET Renewable Power

Generation, vol. 7, pp. 370–379, 2013.

L. Djilali, E. N. Sanchez, and M. Belkheiri, “Real time implementation

of sliding mode field oriented control for a dfig based wind turbine,”

International Transactions on Electrical Energy Systems, vol. 28, 2018.

D. Sun, X. Wang, H. Nian, and Z. Q. Zhu, “A sliding-mode direct

power control strategy for dfig under both balanced and unbalanced grid

conditions using extended active power,” IEEE Transactions on Power

Electronics, vol. 33, p. 1313–1322, 2018.

A. Levant, “Sliding order and sliding accuracy in sliding mode control,”

International Journal of Control, vol. 58, pp. 1247–1263, 1993.

X. Zheng, W. Wei, and D. Xu, “Higher-order sliding mode control of

dfig wind energy system under lvrt,” in 2010 Asia-Pacific Power and

Energy Engineering Conference, (Chengdu, China), 28-31 March 2010.

L. Xiong, P. Li, and J. Wang, “High-order sliding mode control of

dfig under unbalanced grid voltage conditions,” International Journal

of Electrical Power & Energy Systems, vol. 117, pp. 1–13, May. 2020.

J. E. Ruiz-Duarte and A. G.Loukianov, “Output-feedback discrete-time

sliding mode control via disturbance estimation,” in 2018 15th

International Workshop on Variable Structure Systems (VSS), (Graz,

Austria), 9-11 July 2018.

E. N. Sanchez, A. Y. Alanis, and A. G. Loukianov, Discrete-Time High

Order Neural Control trained with Kalman filtering. Verlag London,

Uk: Springer Science & Business Media, 2008.

H. Benbouhenni, Z. Boudjema, and A. Belaidi, “Neuro-second order

sliding mode control of a dfig supplied by a two-level nsvm inverter

for wind turbine system,” Iranian Journal of Electrical and Electronic

Engineering, vol. 14, pp. 362–373, Dec. 2018.

E. N. Sanchez and F. Ornelas-Tellez, Discrete-Time Inverse Optimal

Control for Nonlinear Systems. Boca Raton, FL, USA: CRC press Taylor

and Francis Group, 2013.

J. E. Slotine, “Sliding controller design for non-linear system,”

International Journal of Control, vol. 40, pp. 421–434, Apr. 1984.

B. R. Barmish and G. Leitmann, “On ultimate boundedness control

of uncertain systems in the absence of matching condition,” IEEE

Transactions on Automatic Control, vol. 27, pp. 153–158, Feb. 1982.

P. Wilfrid and B. Jean-Pierre, Higher order sliding modes. New York,

USA: Marcel Dekker, 2002.

C. E. Castaneda, A. G. Loukianov, E. N. Sanchez, and

B. Castillo-Toledo, “Discrete-time neural sliding-mode block control

for a dc motor with controlled flux,” IEEE Transactions on Industrial

Electronics, vol. 59, pp. 1194–1207, 2012.

S. Haykin, Kalman filtering and neural networks, vol. 47. Toronto,

Canada: John Wiley, 2004.

K. Senthilnathan and K. I. Annapoorani, “A review on back-to-back

converters in permanent magnet synchronous generator based

wind energy conversion system,” Indonesian Journal of Electrical

Engineering and Computer Science, vol. 3, pp. 583–591, Jun. 2016.

R. Ruiz-Cruz, E. N. Sanchez, A. Loukianov, and J. A. Ruz-Hernandez,

“Real-time neural inverse optimal control for a wind generator,” IEEE

Transactions on Sustainable Energy, vol. 10, pp. 1172–1183, 2019.

L. Djilali, E. N. Sanchez, and M. Belkheiri, “First and high order sliding

mode control of a dfig-based wind turbine,” Electric Power Components

and Systems, vol. 48, pp. 105–116, 2020.

Downloads

Published

2021-09-01

How to Cite

Djilali, L., Badillo-Olvera, A., Yuliana Rios, Y., López-Beltrán, H. ., & Saihi, L. (2021). Neural High Order Sliding Mode Control for Doubly Fed Induction Generator based Wind Turbines. IEEE Latin America Transactions, 20(2), 223–232. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5456

Issue

Vol. 20 No. 2 (2022): Ordinary Issue

Section

Articles