Data–Driven Techniques for the Fault Diagnosis of a Wind Turbine Benchmark

Babuška, R. (1998). Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston, MA.10.1007/978-94-011-4868-9Search in Google Scholar

Beghelli, S., Guidorzi, R.P. and Soverini, U. (1990). The Frisch scheme in dynamic system identification, Automatica 26(1): 171-176.10.1016/0005-1098(90)90168-HSearch in Google Scholar

Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA.10.1007/978-1-4757-0450-1Search in Google Scholar

Blanke, M., Kinnaert, M., Lunze, J. and Staroswiecki, M. Schr¨oder, J. (2003). Diagnosis and Fault-Tolerant Control, 1st Edn., Springer, Berlin.Search in Google Scholar

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-2016-0041.10.1515/amcs-2016-0041Open DOISearch in Google Scholar

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/s17102202.10.3390/s17102202567744628946673Search in Google Scholar

Chen, J. and Patton, R.J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Boston, MA.10.1007/978-1-4615-5149-2Search in Google Scholar

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-1002.03469.10.3182/20110828-6-IT-1002.03469Open DOISearch in Google Scholar

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.2006.874211.10.1109/TEC.2006.874211Open DOISearch in Google Scholar

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.Search in Google Scholar

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: 10.1109/87.865858.10.1109/87.865858Open DOISearch in Google Scholar

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.2013.2238871.10.1109/TIE.2013.2238871Search in Google Scholar

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/s13042-011-0041-0.10.1007/s13042-011-0041-0Open DOISearch in Google Scholar

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-2016-0004.10.1515/amcs-2016-0004Open DOISearch in Google Scholar

Haykin, S. (2001). Kalman Filtering and Neural Networks, Wiley-Interscience, New York, NY. Hunt, K., Sbarbaro, D., Zbikowki, R. and Gawthrop, P. (1992).Search in Google Scholar

Neural networks for control system: A survey, IEEE Transactions on Neural Networks 28(6): 1083-1112.10.1016/0005-1098(92)90053-ISearch in Google Scholar

Ioannou, P. and Sun, J. (1996). Robust Adaptive Control, Prentice-Hall, Upper Saddle River, NJ.Search in Google Scholar

Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ.Search in Google Scholar

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/s13042-011-0024-1.10.1007/s13042-011-0024-1Open DOISearch in Google Scholar

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-1002.02560.10.3182/20110828-6-IT-1002.02560Open DOISearch in Google Scholar

Ljung, L. (1999). System Identification: Theory for the User, 2nd Edn., Prentice Hall, Englewood Cliffs, NJ.Search in Google Scholar

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.2015.09.671.10.1016/j.ifacol.2015.09.671Open DOISearch in Google Scholar

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.Search in Google Scholar

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-2028.00015.10.3182/20120829-3-MX-2028.00015Search in Google Scholar

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.Search in Google Scholar

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): 1221-1228.10.1109/TCST.2014.2361291Search in Google Scholar

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.2013.2259235.10.1109/TCST.2013.2259235Open DOISearch in Google Scholar

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-1002.01758.10.3182/20110828-6-IT-1002.01758Open DOISearch in Google Scholar

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): 305-315.10.1109/TIE.2010.2045318Search in Google Scholar

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: 10.1243/1748006XJRR98.10.1243/1748006XJRR98Open DOISearch in Google Scholar

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.2009.04.011.10.1016/j.conengprac.2009.04.011Open DOISearch in Google Scholar

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.Search in Google Scholar

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.10.1016/S0165-1684(00)00013-XOpen DOISearch in Google Scholar

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: 10.1155/2012/504368.10.1155/2012/504368Open DOISearch in Google Scholar

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-2013-0032.10.2478/amcs-2013-0032Open DOISearch in Google Scholar

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.2013.08.009.10.1016/j.conengprac.2013.08.009Open DOISearch in Google Scholar

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.2993.10.1002/rnc.2993Open DOISearch in Google Scholar

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/app8010029.10.3390/app8010029Open DOISearch in Google Scholar

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.Search in Google Scholar

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.2014.2364548.10.1109/TIE.2014.2364548Open DOISearch in Google Scholar

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.2014.12.001.10.1016/j.segan.2014.12.001Open DOISearch in Google Scholar

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.Search in Google Scholar

Stamatis, D.H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd Edn., ASQ Quality Press, Milwaukee, WI.Search in Google Scholar

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-1002.00618.10.3182/20110828-6-IT-1002.00618Open DOISearch in Google Scholar

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): 116-132.10.1109/TSMC.1985.6313399Search in Google Scholar

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-2017-0004.10.1515/amcs-2017-0004Open DOISearch in Google Scholar

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.Search in Google Scholar

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-1002.02808.10.3182/20110828-6-IT-1002.02808Open DOISearch in Google Scholar