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Data-driven techniques for the fault diagnosis of a wind turbine benchmark

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.
Rocznik
Strony
247--268
Opis fizyczny
Bibliogr. 48 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Engineering, University of Ferrara, Via Saragat 1/E, 44124 Ferrara, Italy
autor
  • Department of Engineering, University of Ferrara, Via Saragat 1/E, 44124 Ferrara, Italy
autor
  • Department of Electronics, Computer Science and Systems, University of Bologna, Via Fontanelle 40, 47100 Forlì, Italy
Bibliografia
  • [1] Babuška, R. (1998). Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston, MA.
  • [2] Beghelli, S., Guidorzi, R.P. and Soverini, U. (1990). The Frisch scheme in dynamic system identification, Automatica 26(1): 171–176.
  • [3] Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA.
  • [4] Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. Schröder, J. (2003). Diagnosis and Fault-Tolerant Control, 1st Edn., Springer, Berlin.
  • [5] Byrski, J. and Byrski, W. (2016). A double window state observer for detection and isolation of abrupt changes in parameters, International Journal of Applied Mathematics and Computer Science 26(3): 585–602, DOI: 10.1515/amcs-2016-0041.
  • [6] Castaldi, P., Mimmo, N. and Simani, S. (2017). Avionic air data sensors fault detection and isolation by means of singular perturbation and geometric approach, Sensors 17(10): 1–19, DOI: 10.3390/s17102202.
  • [7] Chen, J. and Patton, R.J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Boston, MA.
  • [8] Chen, W., Ding, S.X., Sari, A.H.A., Naik, A., Khan, A.Q. and S., Y. (2011). Observer-based FDI schemes for wind turbine benchmark, Proceedings of the 18th IFAC World Congress 2011, Milan, Italy, Vol. 18, pp. 7073–7078, DOI: 10.3182/20110828-6-IT-1002.03469.
  • [9] Dolan, D.S.L. and Lehn, P.W. (2006). Simulation model of wind turbine 3p torque oscillations due to wind shear and tower shadow, IEEE Transactions on Energy Conversion 21(3): 2050–2057, DOI: 10.1109/TEC.2006.874211.
  • [10] Fantuzzi, C. and Rovatti, R. (1996). On the approximation capabilities of the homogeneous Takagi–Sugeno model, Proceedings of the 5th IEEE International Conference on Fuzzy Systems, New Orleans, LA, USA, pp. 1067–1072.
  • [11] Fantuzzi, C., Simani, S., Beghelli, S. and Rovatti, R. (2002). Identification of piecewise affine models in noisy environment, International Journal of Control 75(18): 1472–1485, DOI: 10.1109/87.865858.
  • [12] Gong, X. and Qiao, W. (2013). Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals, IEEE Transactions on Industrial Electronics 60(8): 3419–3428, DOI: 10.1109/TIE.2013.2238871.
  • [13] Graaff, A.J. and Engelbrecht, A.P. (2012). Clustering data in stationary environments with a local network neighbourhood artificial immune system, International Journal of Machine Learning and Cybernetics 3(1): 1–26, DOI: 10.1007/s13042–011–0041–0.
  • [14] Hassanabadi, A.H., Shafiee, M. and Puig, V. (2016). Robust fault detection of singular LPV systems with multiple time-varying delays, International Journal of Applied Mathematics and Computer Science 26(1): 45–61, DOI: 10.1515/amcs-2016-0004.
  • [15] Haykin, S. (2001). Kalman Filtering and Neural Networks, Wiley-Interscience, New York, NY.
  • [16] Hunt, K., Sbarbaro, D., Zbikowki, R. and Gawthrop, P. (1992). Neural networks for control system: A survey, IEEE Transactions on Neural Networks 28(6): 1083–1112.
  • [17] Ioannou, P. and Sun, J. (1996). Robust Adaptive Control, Prentice-Hall, Upper Saddle River, NJ.
  • [18] Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ.
  • [19] Jun, W., Shitong, W. and Chung, F.-L. (2011). Positive and negative fuzzy rule system, extreme learning machine and image classification, International Journal of Machine Learning and Cybernetics 2(4): 261–271, DOI: 10.1007/s13042–011–0024–1.
  • [20] Laouti, N., Sheibat-Othman, N. and Othman, S. (2011). Support vector machines for fault detection in wind turbines, Proceedings of the 18th IFAC World Congress 2011, Milan, Italy, Vol. 18, pp. 7067–7072, DOI: 10.3182/20110828-6-IT-1002.02560.
  • [21] Ljung, L. (1999). System Identification: Theory for the User, 2nd Edn., Prentice Hall, Englewood Cliffs, NJ.
  • [22] Odgaaard, P.F. and Shafiei, S.E. (2015). Evaluation of wind farm controller based fault detection and isolation, Proceedings of the IFAC SAFEPROCESS Symposium 2015, Paris, France, Vol. 48, pp. 1084–1089, DOI: 10.1016/j.ifacol.2015.09.671.
  • [23] Odgaard, P.F. (2012). FDI/FTC wind turbine benchmark modelling, in R.J. Patton (Ed.), Workshop on Sustainable Control of Offshore Wind Turbines, Vol. 1, University of Hull, Hull, pp. 1–5.
  • [24] Odgaard, P.F. and Stoustrup, J. (2012). Results of a wind turbine FDI competition, Proceedings of the 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, SAFEPROCESS 2012, Mexico City, Mexico, Vol. 8, pp. 102–107, DOI: 10.3182/20120829-3-MX-2028.00015.
  • [25] Odgaard, P.F. and Stoustrup, J. (2013). Fault tolerant wind farm control—a benchmark model, Proceedings of the IEEE Multiconference on Systems and Control, MSC2013, Hyderabad, India, pp. 1–6.
  • [26] Odgaard, P.F. and Stoustrup, J. (2015). A benchmark evaluation of fault tolerant wind turbine control concepts, IEEE Transactions on Control Systems Technology 23(3): 1221–1228.
  • [27] 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, DOI: 10.1109/TCST.2013.2259235.
  • [28] Ozdemir, A.A., Seiler, P. and Balas, G.J. (2011). Wind turbine fault detection using counter-based residual threshold-ing, Proceedings of the 18th IFAC World Congress 2011, Milan, Italy, Vol. 18, pp. 8289–8294, DOI: 10.3182/20110828-6-IT-1002.01758.
  • [29] Parker, M.A., Chong, H.N. and Ran, L. (2011). Fault-tolerant control for a modular generator-converter scheme for direct-drive wind turbines, IEEE Transactions on Industrial Electronics 58(1): 305–315.
  • [30] Patton, R.J., Uppal, F.J., Simani, S. and Polle, B. (2008). Reliable fault diagnosis scheme for a spacecraft attitude control system, Journal of Risk and Reliability 222(2): 139–152, DOI: 10.1243/1748006XJRR98.
  • [31] Patton, R.J., Uppal, F.J., Simani, S. and Polle, B. (2010). Robust FDI applied to thruster faults of a satellite system, Control Engineering Practice 18(9): 1093–1109, DOI: 10.1016/j.conengprac.2009.04.011.
  • [32] Rovatti, R. (1996). Takagi–Sugeno models as approximators in Sobolev norms: The SISO case, 5th IEEE International Conference on Fuzzy Systems, New Orleans, LO, USA, Vol. 2, pp. 1060–1066.
  • [33] Rovatti, R., Fantuzzi, C. and Simani, S. (2000). High-speed DSP-based implementation of piecewise-affine and piecewise-quadratic fuzzy systems, Signal Processing Journal 80(6): 951–963, DOI: 10.1016/S0165-1684(00)00013-X.
  • [34] Simani, S. (2012). Application of a data-driven fuzzy control design to a wind turbine benchmark model, Advances in Fuzzy Systems 2012 : 1–12, DOI: 10.1155/2012/504368.
  • [35] Simani, S. (2013). Residual generator fuzzy identification for automotive diesel engine fault diagnosis, International Journal of Applied Mathematics and Computer Science 23(2): 419–438, DOI: 10.2478/amcs-2013-0032.
  • [36] Simani, S. and Castaldi, P. (2013). Data-driven and adaptive control applications to a wind turbine benchmark model, Control Engineering Practice 21(12): 1678–1693, DOI: dx.doi.org/10.1016/j.conengprac.2013.08.009.
  • [37] 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, DOI: 10.1002/rnc.2993.
  • [38] Simani, S. and Castaldi, P. (2018). Robust control examples applied to a wind turbine simulated model, Applied Sciences 8(1): 1–28, DOI: 10.3390/app8010029.
  • [39] Simani, S. and Diversi, R. (2003). Residual generation and identification for dynamic processes, 5th Symposium on Fault Detection Supervision and Safety for Technical Processes, SAFEPROCESS 2003, Washington, DC, USA, Vol. 1, pp. 375–380.
  • [40] Simani, S., Farsoni, S. and Castaldi, P. (2014). Fault diagnosis of a wind turbine benchmark via identified fuzzy models, IEEE Transactions on Industrial Electronics 62(6): 3775–3782, DOI: 10.1109/TIE.2014.2364548.
  • [41] Simani, S., Farsoni, S. and Castaldi, P. (2015). Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques, Sustainable Energy, Grids and Networks 1(1): 45–52, DOI: 10.1016/j.segan.2014.12.001.
  • [42] Simani, S. and Turhan, C. (2017). Adaptive signal processing strategy for a wind farm system fault accommodation, Proceedings of the Intelligent Systems Conference, IntelliSys 2017, London, UK, pp. 1–8.
  • [43] Stamatis, D.H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd Edn., ASQ Quality Press, Milwaukee, WI.
  • [44] Svard, C. and Nyberg., M. (2011). Automated design of an FDI system for the wind turbine benchmark, Proceedings of the 18th IFAC World Congress 2011, Milan, Italy, Vol. 18, pp. 8307–8315, DOI: 10.3182/20110828-6-IT-1002.00618.
  • [45] Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on System, Man and Cybernetics SMC-15(1): 116–132.
  • [46] Xu, F., Puig, V., Ocampo-Martinez, C., Olaru, S. and Niculescu, S.-I. (2017). Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation, International Journal of Applied Mathematics and Computer Science 27(1): 43–61, DOI: 10.1515/amcs-2017-0004.
  • [47] Xu, J.-X., Liu, C. and Hang, C. (1994). Combined adaptive and fuzzy control using multiple models, 3rd IEEE International Conference on Fuzzy Systems, Orlando, FL, USA, pp. 22–29.
  • [48] Zhang, X., Zhang, Q., Zhao, S., Ferrari, R. M.G., Polycarpou, M.M. and Parisini, T. (2011). Fault detection and isolation of the wind turbine benchmark: An estimation-based approach, Proceedings of the 18th IFAC World Congress 2011, Milan, Italy, Vol. 18, pp. 8295–8300, DOI: 10.3182/20110828-6-IT-1002.02808.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-85845032-f6eb-4a29-bd3e-2e35dacb3ce4
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