Active fault tolerance control of a wind turbine system using an unknown input observer with an actuator fault

• Autorzy: Li S. , Wang H. , Aitouche A. , Tian Y. , Christov N.

Identyfikatory

DOI

10.2478/amcs-2018-0005

• Języki publikacji: EN

Abstrakty

EN

This paper proposes a fault tolerant control scheme based on an unknown input observer for a wind turbine system subject to an actuator fault and disturbance. Firstly, an unknown input observer for state estimation and fault detection using a linear parameter varying model is developed. By solving linear matrix inequalities (LMIs) and linear matrix equalities (LMEs), the gains of the unknown input observer are obtained. The convergence of the unknown input observer is also analysed with Lyapunov theory. Secondly, using fault estimation, an active fault tolerant controller is applied to a wind turbine system. Finally, a simulation of a wind turbine benchmark with an actuator fault is tested for the proposed method. The simulation results indicate that the proposed FTC scheme is efficient.

Słowa kluczowe

EN

wind turbine system; linear parameter varying system; fault tolerant control; unknown input observer

PL

elektrownia wiatrowa; sterowanie tolerujące uszkodzenia; obserwator wejściowy

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2018
• Tom: Vol. 28, no. 1
• Strony: 69--81
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr.

Twórcy

autor

Li S.

• Sino-French International Joint Laboratory of Automatic Control and Signal Processing, Nanjing University of Technology and Science, Nanjing, 210094, China

autor

Wang H.

• Sino-French International Joint Laboratory of Automatic Control and Signal Processing, Nanjing University of Technology and Science, Nanjing, 210094, China

autor

Aitouche A.

• Research Center in Computer Science, Signal and Automatic Control (CRIStAL), French School of High Studies in Engineering, 13 rue de Toul, BP 41290, 59014 Lille Cedex, France

autor

Tian Y.

• Sino-French International Joint Laboratory of Automatic Control and Signal Processing, Nanjing University of Technology and Science, Nanjing, 210094, China

autor

Christov N.

• Research Center in Computer Science, Signal and Automatic Control (CRIStAL), University of Lille 1, Batiment P2, 59655 Villeneuve d’Ascq Cedex, France

Bibliografia

• [1] Armeni, S., Casavola, A. and Mosca, E. (2009). Robust fault detection and isolation for LPV systems under a sensitivity constraint, International Journal of Adaptive Control and Signal Processing 23(1): 55–72.
• [2] Bianchi, F.D., Mantz, R.J. and De Battista, H. (2007). Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design, Springer, London.
• [3] Blesa, J., Rotondo, D., Puig, V. and Nejjari, F. (2014). FDI and FTC of wind turbines using the interval observer approach and virtual actuators/sensors, Control Engineering Practice 24(1): 138–155.
• [4] Boulkroune, B., Djemili, I., Aitouche, A. and Cocquempot, V. (2013). Robust nonlinear observer design for actuator fault detection in diesel engines, International Journal of Applied Mathematics and Computer Science 23(3): 557–569, DOI: 10.2478/amcs-2013-0042.
• [5] Chen, W. and Saif, M. (2006). Fault detection and isolation based on novel unknown input observer design, Proceedings of the American Control Conference, Minnesota, MN, USA, pp. 245–250.
• [6] Bianchi, F.D. and De Battista, R.J.M. (2007). Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design, Springer, London.
• [7] Georg, S. and Schulte, H. (2013). Actuator fault diagnosis and fault-tolerant control of wind turbines using a Takagi–Sugeno sliding mode observer, 2013 Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France, pp. 516–522.
• [8] Georg, S. and Schulte, H. (2014). Takagi–Sugeno sliding mode observer with a weighted switching action and application to fault diagnosis for wind turbines, in J. Korbicz and M. Kowal (Eds.), Intelligent Systems in Technical and Medical Diagnostics, Springer, Berlin, pp. 41–52.
• [9] Georges, J.-P., Theilliol, D., Cocquempot, V., Ponsart, J.-C. and Aubrun, C. (2011). Fault tolerance in networked control systems under intermittent observations, International Journal of Applied Mathematics and Computer Science 21(4): 639–648, DOI: 10.2478/v10006-011-0050-x.
• [10] Hamdi, H., Rodrigues, M., Mechmeche, C., Theilliol, D. and Braiek, N.B. (2012). Fault detection and isolation in linear parameter-varying descriptor systems via proportional integral observer, International Journal of Adaptive Control & Signal Processing 26(3): 224240.
• [11] Hassanabadi, A.H., Shafiee, M. and Puig, V. (2016). UIO design for singular delayed LPV systems with application to actuator fault detection and isolation, International Journal of Systems Science 47(1): 107–121.
• [12] Jonkman, J., Butterfield, S., Musial, W. and Scott, G. (2009). Definition of a 5-MW reference wind turbine for offshore system development, Technical Report No. NREL/TP-500-38060, National Renewable Energy Laboratory, Golden, CO.
• [13] Kamal, E. and Aitouche, A. (2013). Robust fault tolerant control of DFIG wind energy systems with unknown inputs, Renewable Energy 56(4): 2–15.
• [14] Kamal, E., Aitouche, A., Ghorbani, R. and Bayart, M. (2012). Robust fuzzy fault-tolerant control of wind energy conversion systems subject to sensor faults, IEEE Transactions on Sustainable Energy 3(2): 231–241.
• [15] Kamal, E., Aitouche, A., Ghorbani, R. and Bayart, M. (2014). Fuzzy scheduler fault-tolerant control for wind energy conversion systems, IEEE Transactions on Control Systems Technology 22(1): 119–131.
• [16] Khare, V., Nema, S. and Baredar, P. (2016). Solar-wind hybrid renewable energy system: A review, Renewable and Sustainable Energy Reviews 58: 23–33.
• [17] Lofberg, J. (2004). YALMIP: A toolbox for modeling and optimization in Matlab, CACSD Conference, New Orleans, LA, USA, pp. 287–92.
• [18] Odgaard, P.F., Stoustrup, J. and Kinnaert, M. (2009). Fault tolerant control of wind turbines—A benchmark model, IFAC Proceedings Volumes 42(8): 155–160.
• [19] Odgaard, P.F., Stoustrup, J. and Kinnaert, M. (2013). Fault-tolerant control of wind turbines: A benchmark model, IEEE Transactions on Control Systems Technology 21(4): 1168–1182.
• [20] Shi, F. and Patton, R. (2015). An active fault tolerant control approach to an offshore wind turbine model, Renewable Energy 75(C): 788–798.
• [21] Simani, S. and Castaldi, P. (2012). Data-driven design of fuzzy logic fault tolerant control for a wind turbine benchmark, IFAC Proceedings Volumes 45(20): 108–113.
• [22] Simani, S. and Castaldi, P. (2014). Active actuator fault-tolerant control of a wind turbine benchmark model, International Journal of Robust and Nonlinear Control 24(8–9): 1283–1303.
• [23] Simani, S., Farsoni, S. and Castaldi, P. (2013). Robust actuator fault diagnosis of a wind turbine benchmark model, 2013 IEEE 52nd Annual Conference on Decision and Control (CDC), Florence, Italy, pp. 4422–4427.
• [24] Simani, S., Farsoni, S. and Castaldi, P. (2015). Fault diagnosis of a wind turbine benchmark via identified fuzzy models, IEEE Transactions on Industrial Electronics 62(6): 3775–3782.
• [25] Sloth, C., Esbensen, T. and Stoustrup, J. (2011). Robust and fault-tolerant linear parameter-varying control of wind turbines, Mechatronics 21(4): 645–659.
• [26] Zhang, K., Jiang, B., Cocquempot, V. (2008). Adaptive observer-based fast fault estimation, International Journal of Control Automation and Systems 6(3): 320.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).