Development of a new low-cost procedure for wind farm maintenance with a view to decrease soil pollution

Document Type : Original Research Paper

Authors

1 Universidade da Coruña. Escuela Técnica Superior de N. y M. Departamento de Energía y P. M. Paseo de Ronda, 51, 15011. A Coruña, España

2 Department of Geography, Golestan University, Shahid Beheshti 49138-15759, Gorgan, Iran

10.7508/pj.2015.03.004

Abstract

The purpose of this article is to present the development of a wind farm, with a condition monitoring system (CMS) based on control charts as the algorithm, centred on a new index, to prevent soil pollution by oil spills in wind farms. To this end, temperature sensors can be considered as one of the more significant sensors to be employed in this study, because the information obtained with regard to anemometers and electrical power output counters can be employed by the control system. As a result, among the other variables, oil temperatures sampled in multipliers used in the wind turbines of a real wind farm were employed. Statistical analyzes were developed and the relationship between wind farm maintenance (usually related to wind farm oil spills) and oil temperature was obtained. Furthermore, a practical case study, centered in the statistical process control, based on the low-cost sample variable was developed and showed that this new procedure would improve deficiencies in the maintenance process, thus, reducing the failure detection time under low sensor cost, as also the related soil pollution.

Keywords

References

Etkin, D.S.(2008).Oil spill risk analysis for cape wind energy project. International Oil Spill Conference - IOSC 2008, Proceedings. 2008, Pages 571-580.
Jennings, A.D. and Drake, P.R. (1997). Machine tool condition monitoring using statistical quality control charts. Int. J. Mach. Tool. Manu., 37(9), 1243-1249.
Harris, T.J., Seppala, C.T. and Desborough, L.D.(1999). A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J. Process. Contr., 9(2), 1-17.
Kumar Sharma, R., Kumar, D. and Kumar, P.(2008). Predicting uncertain behaviour of industrial system using FM- A practical case. Appl. Soft. Comput., 8(3),96-109.
Lee, S., Lee, J.K., Park, B., Lee, D., Kim, S. and Lee, K. (2003). Development of internet-based ship technical information management system. Ocean. Eng., 33, 1814-1828.
Louit, D.M., Pascual, R. and Jardine, A.K.S. (2009). A practical procedure for the selection of time to failure models based on the assessment of trends in maintenance data. Reliab. Eng. Syst. Safe., 94(10), 1618-1628.
Montgomery, D.C.(1991). Introduction to Statistical Quality Control. (2nd.ed.), Wiley, New York, 369–374.
Orosa, J.A., Oliveira, A.C. and Costa, A.M. (2010). New Procedure for Wind Farm Maintenance. Ind. Manag. Data. Syst, 110(6), 861-882.
Philip. S(1995).Wind power makes its mark in Europe. Phys. World., 8(10), 29-32.
Teresa, H. (2007).Wind turbines: designing with maintenance in mind. Power. Eng., http://www.power-eng.com/articles/print/volume-111/issue-5/features/wind-turbines-designing-with-maintenance-in-mind.html [15 November 2011].
Tavner, P.J., Xiang, J. and Spinato, F. (2007). Reliability analysis for wind turbines. Wind. Ene., 10(81),1-18.
UNEEN15341, (2008). Maintenance-Maintenance Key Performance Indicators.
Verbruggen, T.W. (2003). Wind turbine operation & maintenance based on condition monitoring. Final report, ECN-C-03-047.
http://www.ecn.nl/docs/library/report/2003/c03047.pdf. [15 November 2011]
Vosniakos, G., Wang, J., 1997. A software system framework for planning and operation of quality control in discrete part manufacturing. Comput. Integr. Manuf. Sys., 10 (1), 9-25.
Verma, M.P. (2003). Steam tables for pure water as an ActiveX component in Visual Basic 6.0. Geotermia, Instituto de Investigaciones Electricas. Comput. Geosci., 29(9), 1155-1163.
Verhoefa, J., Degrèvea, B., Huybrechsa, H., Van Veenb, P. and Van der Bruggena, B.( 2007). Simulation of a hybrid pervaporation–distillation process. Com. Chemical. Eng., 32(6), 1135-1146.
Wind Energy Foundation; Improving Regional Air Quality with Wind Energy 2014).http://www. wind energy foundation.org/wind-at-work/public-health-safety/public-health/air-quality.(access 15 December 2014)
Wu, Y. and Liua, L. (2009). Development and evaluation of an ergonomic software package for predicting multiple-task human performance and mental workload in human–machine interface design and evaluation. Comput. Ind. Eng., 56(1), 1-26
.

Files

Share

How to cite

Statistics