Skip to main content

Advertisement

Log in

Bond Graph Algorithms for Fault Detection and Isolation in Wind Energy Conversion

  • Research Article - Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The use of wind energy has increased during the last years; however, wind power varies greatly throughout the day creating important intermittence problems. This paper deals with the modeling, fault detection and isolation of wind turbine generation systems by bond graph approach. The modeling of the wind phenomenon, the turbine mechanical system and the electrical machine, along with the corresponding converter and electrical grid are described, and the problem of fault diagnosis in wind energy conversion is addressed. One of the original points in this work is the use of a new fault detection and isolation method. The proposed method avoids the exploration of all the combinations for its application to the diagnostic of this system operation. The causal paths are used to generate the analytical redundancy relations at each computation step based on the constitutive and structural junction relations. This is shown through an algorithm for monitoring the system by sensor placements on the corresponding bond graph model. The performance of the developed algorithm is evaluated on a model of a commercial sized 4.8 MW wind turbine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Sahin A.Z., Bolat A., Al-Ahmari A.: Investigation of the capacity of underground water pumping using wind energy in Dhahran. Arab. J. Sci. Eng. 36, 879–889 (2011)

    Article  Google Scholar 

  2. Taskin, S.; Dursun, B.; Alboyaci, B.: Performance assessment of a combined solar and wind system. Arab. J. Sci. Eng. 34(1B) (2009)

  3. Luo, M.; Wang, D.W.; Pham, M.; Low, C.B.; Zhang, J.B.: Model based fault diagnosis/prognosis for wheeled mobile robots: a review. In: The 31st Annual Conference of IEEE Industrial Electronics Society (IECON05), pp. 2267–2272 (2005)

  4. Xiyun, Y.; Xiaojuan, H.; Haining, Z.; Xu, D.: Fault diagnosis based on bond graph for feedwater. IEEE International Conference on Control and Automation, Guangzhou (2007)

  5. Odgaard, P.F.; Stoustrup, J.; Kinnaert, M.: Fault tolerant control of wind turbines—a benchmark model. In: Proccedings of the IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona (2009)

  6. Wei, X.; Verhaegen, M.; van den Engelen, T.: Sensor fault diagnosis of wind turbines for fault tolerant. In: Proceedings of the 17th World Congress. The International Federation of Automatic Control, Seoul (2008)

  7. Odgaard, P.F.; Stoustrup, J.; Nielsen, R.; Damgaard, C.: Observer based detection of sensor faults in wind turbines. In: Proceedings of European Wind Energy Conference, Marseille (2009)

  8. Dobrila, C.; Stefansen, R.: Fault tolerant wind turbine control. Master’s thesis, Technical University of Denmark, Kgl. Lyngby (2007)

  9. Gertler, J.: Analytical redundancy methods in fault detection and isolation. In: Proceeding of IFAC/IAMCS Symposium on Safe Processes, pp. 91–103 (1991)

  10. Gertler J., Singer D.A.: New structural framework for parity equation-based failure detection and isolation. Automatica 26, 381–388 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  11. Basseville M.: Detecting changes in signals and systems—a survey. Automatica 24, 309–326 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  12. Willsky A.: A survey of design methods for failure detection in dynamic systems. Automatica 12, 601–611 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  13. Isermann R.: Process fault detection based on modeling and estimation methods: a survey. Automatica 20, 387–404 (1984)

    Article  MATH  Google Scholar 

  14. Young P.: Parameter estimation for continuous time models—a survey. Automatica 17, 23–39 (1981)

    Article  MATH  Google Scholar 

  15. Venkatasubramanian V., Rengaswamy R., Kavuri S.: A review of process fault detection and diagnosis. Part II. Qualitative models and search strategies. Comput. Chem. Eng. 27, 313–326 (2003)

    Article  Google Scholar 

  16. Venkatasubramanian, V.; Rengaswamy, R.; Kavuri, S.; Yin, K.: A review of process fault detection and diagnosis. Part III. Process history based methods. Comput. Chem. Eng. 27, 327–346 (2003)

    Google Scholar 

  17. Venkatasubramanian, V.; Rengaswamy, R.; Yin, K.; Kavuri, S.: A review of process fault detection and diagnosis. Part I. Quantitative model-based methods. Comput. Chem. Eng. 27, 293–311 (2003)

    Google Scholar 

  18. Samantaray A.K., Medjaher K., Bouamama B.O., Staroswiecki M., Tanguy G.D.: Diagnostic bond graphs for online fault detection and isolation. Simul. Model. Pract. Theory 14(3), 237–262 (2006)

    Article  Google Scholar 

  19. Muyeen, S.M.; Tamura, J.; Murata, T.: Stability Augmentation of a Grid-connected Wind Farm. Springer, London (2008)

  20. Paynter, H.M.: Analysis and Design of Engineering Systems. MIT Press, Cambridge (1961)

  21. Bouamama, B.; Busson, F.; Dauphin-Tanguy, G.; Staroswiecki, M.: Analysis of structural properties of thermodynamic bond graph models. In: Proceeding of the 4th IFAC: Fault Detection Supervision and Safety for Technical Processes, vol. 2. IFAC, Budapest, pp. 1068–1073 (2000)

  22. Zitouni, N.; Khiari, B.; Andoulsi, R.; Sellami, A.; Mami, A.; Hssen, A.: Modelling and non linear control of a photovoltaic system with storage batteries: A bond graph approach. IJCSNS Int. J. Comput. Sci. Netw. Secur. 11(6), 105–114 (2011)

  23. Thoma, J.: Introduction to Bond Graphs and Their Applications. Pergam on Press, Oxford (1975)

  24. Karnopp, D.; Rosenberg, R.: System Dynamics: A Unified Approach. Wiley, New York (1974)

  25. Karnopp, D.C.; Margolis, D.L.; Rosenberg, R.C.: System Dynamics Modeling and Simulation of Mechatronics Systems. Wiley, New Jersey (2006)

  26. Akinci T.C., Selcuk Nogay H.: Wind speed correlation between neighboring measuring stations. Arab. J. Sci. Eng. 37, 1007–1019 (2012)

    Article  Google Scholar 

  27. Leclercq, L.: Apport du stockage inertiel associe à des éoliennes dans un réseau électrique en vue d’assurer des services systèmes. Thése de doctorat (2004)

  28. Davigny, A.: Participation aux services systéme de fermes d’éoliennes à vitesse variable intégrant du stockage inertiel d’énergie; Thèse de doctorat (2007)

  29. Cimuca, G.; Saudemont, C.; Robyns, B.; Radulescu, M.: Control and performance evaluation of a flywheel energy storage system associated to a variable speed wind generator. IEEE Trans. Ind. Electron. 53(4), 1074–1085 (2006)

    Google Scholar 

  30. Vijayalakshmi S., Saikumar S., Saravanan S., Sandip R., Sridhar V.: Modelling and controlof a wind turbine using permanent magnet synchronous generator. Int. J. Eng. Sci. Technol. 3(3), 2377–2384 (2011)

    Google Scholar 

  31. Qiao W., Zhou W., Aller J.M., Harley R.G.: Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG. IEEE Trans. Power Electron. 23(3), 1156–1169 (2008)

    Article  Google Scholar 

  32. Ying, M.; Li, G.; Zhou, M.; Zhao, C.: Modeling of the wind turbine with a permanent magnet synchronous generator for integration. IEEE, pp. 1–6. ISBN 1-4244-1298-6 (2007)

  33. Belhadj, J.; Roboam, X.: Investigation of different methods to control a small variable- speed wind turbine with PMSM drives. ASME Trans. J. Energy Resour. Technol. 129/201 (2007)

  34. Mohan, N.; Undeland, T.; Robbins, W.: Power Electronics: Converters, Applications and Design, 1st edn. Wiley, New York (1995)

  35. Valtchev V., Bossche A., Ghijselen J., Melkebeek J.: Autonomous renewable energy conversion system. Renew. Energy 19(1), 259–275 (2000)

    Article  Google Scholar 

  36. Mezghanni, D.; Andoulsi, R.; Mami, A.; Dauphin-Tanguy, G.: Bond graph modeling of a photovoltaic system feeding an induction motor-pump. Simul. Model. Pract. Theory, 1224–1238 (2007)

  37. González-Contreras, B.M.; Rullán-Lara, J.L.; Vela-Valdés, L.G.; Claudio, S.A.: Modeling, simulation and fault diagnosis of the three-phase inverter using bond graph. IEEE International Symposium on Industrial Electronics (2007)

  38. Sanchez R., Dauphin-Tanguy G., Guillaud X., Colas F.: Bond graph based control of a three-phase inverter with LC filter—connection to passive and active loads. J. Simul. Model. Pract. Theory 18, 1185–1198 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abd Essalam Badoud.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Badoud, A.E., Khemliche, M., Ould Bouamama, B. et al. Bond Graph Algorithms for Fault Detection and Isolation in Wind Energy Conversion. Arab J Sci Eng 39, 4057–4076 (2014). https://doi.org/10.1007/s13369-014-1044-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-014-1044-4

Keywords

Navigation