[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Maintenance of offshore wind turbines is a complex and costly undertaking which acts as a barrier to the development of this source of energy. Factors such as the size of the turbines, the size of the wind farms, their distance from the coast and meteorological conditions make it difficult for the stakeholders to select the optimal maintenance strategy. With the objective of reducing costs and duration of such operations it is important that new maintenance techniques are investigated. In this paper we propose a hybrid model of maintenance that is based on multi-agent systems; this allows for the modelling of systems with dynamic interactions between multiple parts. A multi-criteria decision algorithm has been developed to allow analysis and selection of different maintenance strategies. A cost model that includes maintenance action cost, energy loss and installation of monitoring system cost has been presented. For the purposes of this research we have developed a simulator using NetLogo software and have provided experimental results. The results show that employing the proposed hybrid maintenance strategy could increase wind farm productivity and reduce maintenance cost.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Ainslie, J. F. (1988). Calculating the flowfield in the wake of wind turbines. Journal of Wind Engineering and Industrial Aerodynamics, 27(1), 213–224.

    Article  Google Scholar 

  • Ammara, I., Leclerc, C., & Masson, C. (2002). A viscous three-dimensional differential/actuator-disk method for the aerodynamic analysis of wind farms. Journal of Solar Energy Engineering, 124(4), 345–356.

    Article  Google Scholar 

  • Astariz, S., Perez-Collazo, C., Abanades, J., & Iglesias, G. (2015). Co-located wind-wave farm synergies (operation & maintenance): A case study. Energy Conversion and Management, 91, 63–75.

    Article  Google Scholar 

  • Babu, J. R., & Jithesh, S. (2008). Breakdown risks in wind energy turbines. Pravartak, the Journal of Insurance and risk Management from National Insurance Academy, Pun, 3, 3–23.

  • Besnard, F., Fischer, K., & Tjernberg, L. B. (2013). A model for the optimization of the maintenance support organization for offshore wind farms. Sustainable Energy, IEEE Transactions on, 4(2), 443–450.

    Article  Google Scholar 

  • Braam, H., & Verbruggen, T. W. (2000). R & D NEEDS FOR O & M OF WIND TURBINES

  • Brennan, F. (2013). Risk based maintenance for offshore wind structures. Procedia CIRP, 11, 296–300.

    Article  Google Scholar 

  • Burton, T., Jenkins, N., Sharpe, D., & Bossanyi, E. (2011). Wind energy handbook. Chichester: Wiley.

    Book  Google Scholar 

  • Byon, E., Ntaimo, L., & Ding, Y. (2010). Optimal maintenance strategies for wind turbine systems under stochastic weather conditions. Reliability, IEEE Transactions on, 59(2), 393–404.

    Article  Google Scholar 

  • Byon, E., Pérez, E., Ding, Y., & Ntaimo, L. (2011). Simulation of wind farm operations and maintenance using discrete event system specification. Simulation, 87(12), 1093–1117.

    Article  Google Scholar 

  • Cunha, P., Duarte, J. C., & Alting, L. (2004). Development of a productive service module based on a life cycle perspective of maintenance issues. CIRP Annals-Manufacturing Technology, 53(1), 13–16.

    Article  Google Scholar 

  • Dahane, M., Sahnoun, M., Bettayeb, B., Baudry, D., & Boudhar, H. (2015) Impact of spare parts remanufacturing on the operation and maintenance performance of offshore wind turbines: A multi-agent approach. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1154-1

    Article  Google Scholar 

  • Dimeas, A. L., & Hatziargyriou, N. D. (2005). Operation of a multiagent system for microgrid control. IEEE Transactions on Power Systems PWRS, 20(3), 1447.

    Article  Google Scholar 

  • Energy, C. (2014). The basic components of a turbine. http://www.coriolis-energy.com/wind_energy/wind_technology.html

  • Fécamp, E. D. P. D. (2013). Biln carbone du parc éolien en mer au large de fécamp

  • Feijòo, A. E., Cidràs, J., Dornelas, J. L. G. (1999). Wind speed simulation in wind farms for steady–state security assessment of electrical power systems. IEEE Transactions on Energy conversion, 14(4), 1582–1588.

    Article  Google Scholar 

  • Fischer, K., Besnard, F., & Bertling, L. (2012). Reliability-centered maintenance for wind turbines based on statistical analysis and practical experience. Energy Conversion, IEEE Transactions on, 27(1), 184–195.

    Article  Google Scholar 

  • Gundegjerde, C., Halvorsen, I. B., Halvorsen-Weare, E. E., Hvattum, L. M., & Nonås, L. M. (2015). A stochastic fleet size and mix model for maintenance operations at offshore wind farms. Transportation Research Part C: Emerging Technologies, 52, 74–92.

    Article  Google Scholar 

  • Haddad, G., Sandborn, P., & Pecht, M. (2014). Using maintenance options to maximize the benefits of prognostics for wind farms. Wind Energy, 17(5), 775–791.

    Article  Google Scholar 

  • Hau, E., & Von Renouard, H. (2013). Wind turbines: Fundamentals, technologies, application, economics. Berlin: Springer.

    Book  Google Scholar 

  • Henderson, A. R., Morgan, C., Smith, H. C., Barthelmie, R. J., & Boesmans, B. (2003). Offshore wind energy in Europe—a review of the state-of-the-art. Wind energy, 6(1), 35–52.

    Article  Google Scholar 

  • Herbert, G. J., Iniyan, S., & Amutha, D. (2014). A review of technical issues on the development of wind farms. Renewable and Sustainable Energy Reviews, 32, 619–641.

    Article  Google Scholar 

  • Hyers, R., McGowan, J., Sullivan, K., Manwell, J., & Syrett, B. (2006). Condition monitoring and prognosis of utility scale wind turbines. Energy Materials, 1(3), 187–203.

    Article  Google Scholar 

  • Ivan Pineda, S. A., Moccia, J., & Wilkes, J. (2014). Wind in power 2013 (February):1–12.

  • Jensen, N. O. (1983). A note on wind generator interaction

  • Karki, R., & Patel, J. (2009). Reliability assessment of a wind power delivery system. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 223(1), 51–58. doi:10.1243/1748006XJRR218.

    Article  Google Scholar 

  • Kim, H., Singh, C., & Sprintson, A. (2012). Simulation and estimation of reliability in a wind farm considering the wake effect. Sustainable Energy, IEEE Transactions on, 3(2), 274–282.

    Article  Google Scholar 

  • Kooijman, H., Lindenburg, C., Winkelaar, D., & van der Hooft, E. (2003). Dowec 6 mw pre-design. Energy Research Center of the Netherlands (ECN).

  • Larsen, G. C. (1988). A simple wake calculation procedure (Risø-M; No. 2760). http://orbit.dtu.dk/files/55567186/ris_m_2760.pdf.

  • Lau, B. C. P., Ma E. W. M., & Pecht, M. (2012). Review of offshore wind turbine failures and fault prognostic methods. In Proceedings of the IEEE 2012 prognostics and system health management conference (PHM-2012 Beijing) (pp. 1–5). doi:10.1109/PHM.2012.6228954

  • Liyanage, J. P. (2008). Integrated e-operations-e-maintenance: Applications in north sea offshore assets. In Complex system maintenance handbook (pp. 585–609). Springer.

  • Lu, B., Li, Y., Wu, X., & Yang, Z. (2009). A review of recent advances in wind turbine condition monitoring and fault diagnosis. In Power electronics and machines in wind applications, 2009 (PEMWA 2009) (pp. 1–7). IEEE.

  • Mustafee, N., Sahnoun, M., Smart, A., & Godsiff, P. (2015). An application of distributed simulation for hybrid modeling of offshore wind farms. In Proceedings of the 2015 ACM SIGSIM/PADS conference on principles of advanced discrete simulation, June 10–12, 2015, London (pp.171–172). New York, NY: ACM. doi:10.1145/2769458.2769492.

  • Nielsen, J. J., & Sørensen, J. D. (2011). On risk-based operation and maintenance of offshore wind turbine components. Reliability Engineering & System Safety, 96(1), 218–229.

    Article  Google Scholar 

  • Nilsson, J., & Bertling, L. (2007). Maintenance management of wind power systems using condition monitoring systems mdash;life cycle cost analysis for two case studies. Energy Conversion, IEEE Transactions on, 22(1), 223–229. doi:10.1109/TEC.2006.889623.

    Article  Google Scholar 

  • Palanci, A. (2011). Leak-free hydraulic fittings prevent vibration failure. Windpower Engineering for Parker Hannifin. Cleveland: WTWH Media, Inc.

    Google Scholar 

  • Pérez, M., García, E., Morant, F., Correcher, A., & Quiles, E. (2010). Optimal maintenance system for offshore wind turbines. In International conference on renewable energies and power quality (ICREPQ10). Granada (Spain), 23th to 25th March.

  • Perveen, R., Kishor, N., & Mohanty, S. R. (2014). Off-shore wind farm development: Present status and challenges. Renewable and Sustainable Energy Reviews, 29, 780–792.

    Article  Google Scholar 

  • Petković, D., Ab Hamid, S. H., Ćojbašić, Ž., & Pavlović, N. T. (2014). Adapting project management method and anfis strategy for variables selection and analyzing wind turbine wake effect. Natural Hazards, 74(2), 463–475.

  • Radakovič, M., Obitko, M., & Mařík, V. (2012). Dynamic explicitly specified behaviors in distributed agent-based industrial solutions. Journal of Intelligent Manufacturing, 23(6), 2601–2621.

  • Rademakers, L., Braam, H., Zaaijer, M. B., & Energy, S. W. (2003). Assessment and optimisation of operation and maintenance of offshore wind turbines.

  • Sahnoun, M., Baudry, D., Louis, A., & Mazari, B. (2014a) Modelisation d’un plan de maintenance basee sur les systemes multi-agents pour leseoliennes offshore.

  • Sahnoun, M., Bettayeb, B., Bassetto, S. J., & Tollenaere, M. (2014b). Simulation-based optimization of sampling plans to reduce inspections while mastering the risk exposure in semiconductor manufacturing. Journal of Intelligent Manufacturing. doi:10.1007/s10845-014-0956-x

    Article  Google Scholar 

  • Sahnoun, M., Godsiff, P., Baudry, D., Louis, A., & Mazari, B. (2014c) Modelling of maintenance strategy of offshore wind farms based multi-agent system. In CIE44 & ISSM14 (44th international conference on computers & industrial engineering & 9th international symposiom on intelligent manufacturing and service systems) (591, pp. 2406–2420).

  • Santos, P., Maudes, J., & Bustillo, A. (2015). Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. Journal of Intelligent Manufacturing. doi:10.1007/s10845-015-1110-0

    Article  Google Scholar 

  • Sheng, S. (2013). Report on wind turbine subsystem reliability–a survey of various databases. National Renewable Energy Laboratory, Golden, CO, Tech Rep NREL/PR-5000-59111.

  • Stenberg, A., & Holttinen, H. (2010). Analysing failure statistics of wind turbines in finland. In European wind energy conference (pp. 20–23).

  • Tavner, P. (2012). Offshore wind turbines: Reliability, availability & maintenance. IET Renewable Energy Series.

  • Taylor, J. H., Sayda, & A. F. (2008). Prototype design of a multi-agent system for integrated control and asset management of petroleum production facilities. In American control conference (pp. 4350–4357). IEEE.

  • Thornton, E. B., & Guza, R. (1983). Transformation of wave height distribution. Journal of Geophysical Research: Oceans (1978–2012), 88(C10), 5925–5938.

    Article  Google Scholar 

  • Tian, Z., Lin, D., & Wu, B. (2012). Condition based maintenance optimization considering multiple objectives. Journal of Intelligent Manufacturing, 23(2), 333–340.

    Article  Google Scholar 

  • Tisue, S., & Wilensky, U. (2004). Netlogo: A simple environment for modeling complexity. In International conference on complex systems (pp. 16–21).

  • Trappey, A., Trappey, C., & Ni, W. C. (2013). A multi-agent collaborative maintenance platform applying game theory negotiation strategies. Journal of Intelligent Manufacturing, 24(3), 613–623. doi:10.1007/s10845-011-0606-5.

    Article  Google Scholar 

  • Van de Pieterman, R., Braam, H., Obdam, T., Rademakers, L., & van der Zee, T. (2011). Optimisation of maintenance strategies for offshore wind farms. In The offshore 2011 conference

  • Zhang, X., & Wang, W. (2009). Wind farm and wake effect modeling for simulation of a studied power system. In Power systems conference and exposition. PSCE’09 (pp. 1–6). IEEE: IEEE/PES.

  • Zhou, W., Yang, H., & Fang, Z. (2006). Wind power potential and characteristic analysis of the pearl river delta region, china. Renewable Energy, 31(6), 739–753. doi:10.1016/j.renene.2005.05.006.

    Article  Google Scholar 

Download references

Acknowledgments

Acknowledgement is made to European Union for the support of this research through the European Program INTERREG IVA France-Channel-UK by funding project entitled MER Innovate.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M’hammed Sahnoun.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahnoun, M., Baudry, D., Mustafee, N. et al. Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system . J Intell Manuf 30, 2981–2997 (2019). https://doi.org/10.1007/s10845-015-1171-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-015-1171-0

Keywords

Navigation