Robust sensor Fault Reconstruction of Wind Turbine in the Presence of Uncertainty and Disturbance: Adaptive Sliding Mode Observer Approach

Document Type : Original Article


1 Department of Engineering, University of Zanjan, Zanjan, Iran

2 Faculty of Engineering, University of Zanjan, Zanjan, Iran


In this paper, an approach for robust sensor fault reconstruction of wind turbine systems in the presence of simultaneous uncertainty and disturbance is proposed. For this purpose, an adaptive sliding mode observer is designed such that the fault is reconstructed through an online adaptive law. The significance of the proposed approach in addition to its robustness against the bounded disturbances and uncertainties is that it does not require the fault and uncertainty bounds to be known a priori. An efficient algorithm is presented to adjust the design parameters based on the Linear Matrix Inequality (LMI) concept. The proposed approach is applied to a 5MWs wind turbine system and simulation results demonstrate the accuracy and desirable performance of the approach.


[1]      محسن رحیمی، محمدرضا اسماعیلی، «طراحی کنترل‌کننده توان و بهبود میرایی نوسانات پیچشی در توربین بادیDFIG-710 kW نصب شده در سایت بینالود»، مجله مهندسی برق دانشگاه تبریز، (4) 46، 134-123، 1395.
[2]      F. Bayat and H. Bahmani, “Power regulation and control of wind turbines: LMI‐based output feedback approach,” International Transactions on Electrical Energy Systems, vol. 27, no. 12, 2017, in press.
[3]      J. He, J. Qiu, C. Zhang, and C. Luo, “Robust fault detection using sliding mode and adaptive observers for uncertain nonlinear systems,” Dyn. Continuous. Discrete and Impulsive Syst. Ser. B: App. & Algorithms, vol. 15, no. 5, pp. 709-718, 2008.
[4]      S. Montes de Oca, S. Tornil‐Sin, V. Puig, and D. Theilliol, “Fault‐tolerant control design using the linear parameter varying approach,” International Journal of Robust and Nonlinear Control, vol. 24, no. 14, pp. 1969-1988, 2014.
[5]      یاشار شب بویی, امیر ریخته‌گرغیاثی، سهراب خانمحمدی، «طراحی کنترل‌کننده تحمل‌پذیر خطای مدلغزشی ترمینال غیرتکین برای سیستم‌های غیرخطی برمبنای فیلترکالمن توسعه‌یافته تطبیقی»،مجله مهندسی برق دانشگاه تبریز، 183-173، (4) 46، 1395.
[6]      X. Wei, M. Verhaegen, and T. van Engelen, ”Sensor fault detection and isolation for wind turbines based on subspace identification and Kalman filter techniques,” International Journal of Adaptive Control and Signal Processing, vol. 24, no. 8, pp. 687-707, 2010.
[7]      G. Wheeler, C.Y. Su, and Y. Stepanenko, “A sliding mode controller with improved adaptation laws for the upper bounds on the norm of uncertainties,” Automatica, vol.  34, no. 12, pp. 1657-1661, 1998.
[8]      Y.J. Huang, T.C. Kuo, and S.H. Chang, “Adaptive sliding-mode control for nonlinear systems with uncertain parameters,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 2, pp. 534-539, 2008.
[9]      O. Ozgonenel, E. Kilic, M.A. Khan, and M.A. Rahman, “A new method for fault detection and identification of incipient faults in power transformers,” Electric Power Components and Systems, vol. 36, no. 11, pp. 1226-1244, 2008.
[10]      H. Wang, Z.J. Huang, and S. Daley, “On the use of adaptive updating rules for actuator and sensor fault diagnosis,” Automatica, vol. 33, no. 2, pp. 217-225, 1997.
[11]      X. Zhang, “Sensor bias fault detection and isolation in a class of nonlinear uncertain systems using adaptive estimation,” IEEE Transactions on Automatic Control, vol. 56, no. 5, pp. 1220-1226, 2011.
[12]      H. Ríos, E. Punta, and L. Fridman, “Fault detection and isolation for nonlinear non-affine uncertain systems via sliding-mode techniques,” International Journal of Control, vol. 90, no. 2, pp. 218-230, 2017.
[13]      R. Sharma, and M. Aldeen. “Fault detection in nonlinear systems with unknown inputs using sliding mode observer,” American Control Conference, ACC'07. IEEE, 2007.
[14]      Z.Q. Wu, Y. Yang, and C.H. Xu, “Adaptive fault diagnosis and active tolerant control for wind energy conversion system,” International Journal of Control, Automation and Systems, vol. 13, no. 1, pp. 120-125, 2015.
[15]      M. Witczak, D. Rotondo, V. Puig, F. Nejjari, and M. Pazera, “Fault estimation of wind turbines using combined adaptive and parameter estimation schemes,” International Journal of Adaptive Control and Signal Processing, vol. 32, no. 4, pp. 549-567, 2018.
[16]      P. Kühne, F. Pöschke, and H. Schulte, “Fault estimation and fault‐tolerant control of the FAST NREL 5‐MW reference wind turbine using a proportional multi‐integral observer,” International Journal of Adaptive Control and Signal Processing, vol. 32, no. 4, pp. 568-585, 2018.
[17]      H. Shao, Z. Gao, X. Liu, and K. Busawon, “Parameter-varying modelling and fault reconstruction for wind turbine systems,” Renewable Energy, vol. 116, no. 1, pp. 145-152, 2018.
[18]      F.A. Inthamoussou, F.D. Bianchi, H. De Battista, and R.J. Mantz, “Gain Scheduled H∞ Control of Wind Turbines for the Entire Operating Range,” Wind Turbine Control and Monitoring. Springer International Publishing, pp. 71-95, 2014.
[19]      F. Bayat M. Farkian, “Path planning and control of airborne systems for optimal wind energy extraction,” Journal of Nonlinear Systems in Electrical Engineering (JNSEE), vol. 4, no. 1, pp. 78-96, 2018.
[20]      C.P. Tan, and C. Edwards, “Sliding mode observers for robust detection and reconstruction of actuator and sensor faults,” International Journal of Robust and Nonlinear Control, vol. 13, no. 5, pp. 443-463, 2003.
[21]      K. Zhang, B. Jiang, and V. Cocquempot,  “Adaptive observer-based fast fault estimation,” International Journal of Control, Automation, and Systems, vol. 6, no. 3, pp. 320-326, 2008.