This paper presents the application of data-mining techniques for identification and prediction of status patterns in wind turbines. Early prediction of status patterns benefits turbine maintenance by indicating the deterioration of components. An association rule mining algorithm is used to identify frequent status patterns of turbine components and systems that are in turn predicted using historical wind turbine data. The status patterns are predicted at six time periods spaced at 10 min intervals. The prediction models are generated by five data-mining algorithms. The random forest algorithm has produced the best prediction results. The prediction results are used to develop a component performance monitoring scheme.
1.
Nelson
, D. B.
, Nehrir
, M. H.
, and Wang
, C.
, 2006, “Unit Sizing and Cost Analysis of Stand-Alone Hybrid Wind/PV/Fuel Cell Power Generation Systems
,” Renewable Energy
0960-1481, 31
(10
), pp. 1641
–1656
.2.
Walford
, C. A.
, 2006, “Wind Turbine Reliability: Understanding and Minimizing Wind Turbine Operation and Maintenance Costs
,” Sandia National Laboratory Report No. SAND2006-1100.3.
Herbert
, G. M. J.
, Iniyan
, S.
, Sreevalsan
, E.
, and Rajapandian
, S.
, 2007, “A Review of Wind Energy Technologies
,” Renewable Sustainable Energy Rev.
1364-0321, 11
(6
), pp. 1117
–1145
.4.
Kusiak
, A.
, Li
, W.
, and Song
, Z.
, 2010, “Dynamic Control of Wind Turbines
,” Renewable Energy
0960-1481, 35
(2
), pp. 456
–463
.5.
Huyer
, S. A.
, Simms
, D.
, and Robinson
, M. C.
, 1996, “Unsteady Aerodynamics Associated With a Horizontal-Axis Wind Turbine
,” American Association for Artificial Intelligence Journal
, 34
(7
), pp. 1410
–1419
.6.
Narayanswamy
, R.
, Metz
, J. L.
, and Johnson
, K. M.
, 1998, “Intelligent Data Elimination for a Rare Event Application
,” Proc. SPIE
0277-786X, 3460
, pp. 906
–917
.7.
Tsumoto
, S.
, 2003, “Chance Discovery in Medicine—Detection of Rare Risky Events in Chronic Diseases
,” New Generation Computing
, 21
(2
), pp. 135
–147
.8.
Kusiak
, A.
, and Shah
, S.
, 2006, “A Data-Mining-Based System for Prediction of Water Chemistry Faults
,” IEEE Trans. Ind. Electron.
0278-0046, 53
(2
), pp. 593
–603
.9.
Kusiak
, A.
, 2000, “Decomposition in Data Mining: An Industrial Case Study
,” IEEE Trans. Electron. Packag. Manuf.
1521-334X, 23
(4
), pp. 345
–353
.10.
Tan
, P. N.
, Steinbach
, M.
, and Kumar
, V.
, 2006, Introduction to Data Mining
, Pearson Education/Addison Wesley
, Boston, MA
.11.
Bae
, H.
, Kim
, S.
, Kim
, Y.
, Lee
, M. H.
, and Woo
, K. B.
, 2003, “E-Prognosis and Diagnosis for Process Management Using Data Mining and Artificial Intelligence
,” Proceedings of the Industrial Electronics Conference
, Roanoke, VA, Vol. 3
, pp. 2537
–2542
.12.
Seo
, H.
, Yang
, J.
, and Choi
, J.
, 2001, “Building Intelligent Systems for Mining Information Extraction Rules From Web Pages by Using Domain Knowledge
,” Proceedings of the IEEE International Symposium on Industrial Electronics
, Pusan, South Korea, Vol. 1
, pp. 322
–327
.13.
Landberg
, L.
, 1999, “Short-Term Prediction of the Power Production From Wind Farms
,” J. Wind. Eng. Ind. Aerodyn.
0167-6105, 80
(1–2
), pp. 207
–220
.14.
Mohandes
, M. A.
, Reham
, S.
, and Halawani
, T. O.
, 1998, “A Neural Networks Approach for Wind Speed Prediction
,” Renewable Energy
0960-1481, 13
(3
), pp. 345
–354
.15.
Lange
, M.
, and Focken
, U.
, 2006, Physical Approach to Short-Term Wind Power Prediction
, Springer-Verlag
, Berlin, Heidelberg, Germany
.16.
Barbounis
, T. G.
, Theocharis
, J. B.
, Alexiadis
, M. C.
, and Dokopoulos
, P. S.
, 2006, “Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models
,” IEEE Trans. Energy Convers.
0885-8969, 21
(1
), pp. 273
–284
.17.
Cambell
, P. R. J.
, and Adamson
, K.
, 2003, “Identification of Blade Vibration Causes in Wind Turbine Generators
,” The Fourth International Conference on Data Mining Including Building Applications for CRM and Competitive Intelligence
, Rio de Janeiro, Brazil, Dec. 1–3, Vol. 29
, pp. 149
–158
.18.
Andrawus
, J. A.
, Watson
, J.
, Kishk
, M.
, and Adam
, A.
, 2006, “The Selection of a Suitable Maintenance Strategy for Wind Turbines
,” Wind Eng.
0309-524X, 30
(6
), pp. 471
–486
.19.
Rademakers
, L. W. M. M.
, Braam
, H.
, Zaaijer
, M. B.
, and Bussel
, G. J. W. V.
, 2003, “Assessment and Optimization of Operation and Maintenance of Offshore Wind Turbines
,” Proceedings of the European Wind Energy Conference
, EWEA, Madrid, Spain, Jun. 16–19, pp. 1
–5
.20.
Ribrant
, J.
, 2005, “Reliability Performance and Maintenance—A Survey of Failures in Wind Power Systems
,” MS thesis, KTH School of Electrical Engineering, Sweden.21.
Milborrow
, D. J.
, 2002, “Will Downward Trends in Wind Prices Continue?
,” Windstats Newsletter
, 15
(3
), pp. 1
–3
.22.
Ding
, Y.
, Byon
, E.
, Park
, C.
, Tang
, J.
, Lu
, Y.
, and Wang
, X.
, 2007, “Dynamic Data-Driven Fault Diagnosis of Wind Turbine Systems
,” Proceedings of the Seventh International Conference on Computational Science, Part I: ICCS
, Vol. 4487
, pp. 1197
–1204
.23.
Tavner
, P. J.
, Xiang
, J.
, and Spinato
, F.
, 2007, “Reliability Analysis for Wind Turbines
,” Wind Energy
1095-4244, 10
(1
), pp. 1
–18
.24.
Chen
, H.
, 2000, “Generating System Reliability Optimization
,” Ph.D. thesis, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.25.
Chandler
, H.
, 2003, Wind Energy—The Facts
, European Wind Energy Association
, Brussels, Belgium
.26.
Wilkinson
, M. R.
, and Tavner
, P. J.
, 2005, “Extracting Condition Monitoring Information From a Wind Turbine Drive Train
,” The 39th Universities Power Engineering Conference
, Bristol.27.
Xiang
, D.
, Ran
, L.
, Tavner
, P. J.
, and Yang
, S.
, 2006, “Control of a Doubly Fed Induction Generator in a Wind Turbine during Grid Fault Ride-Through
,” IEEE Trans. Energy Convers.
0885-8969, 21
(3
), pp. 652
–662
.28.
Davies
, A.
, 1998, Handbook of Condition Monitoring
, Chapman & Hall
, London
.29.
Hameed
, Z.
, Hong
, Y. S.
, Cho
, Y. M.
, Ahn
, S. H.
, and Song
, C. K.
, 2009, “Condition Monitoring and Fault Detection of Wind Turbines and Related Algorithms: A Review
,” Renewable Sustainable Energy Rev.
1364-0321, 13
(1
), pp. 1
–39
.30.
Rao
, B. K. N.
, 1996, Handbook of Condition Monitoring
, Elsevier
, Oxford
.31.
Pedersen
, K. O. H.
, and Havemann
, H.
, 2000, “An Alternative Approach to Power Engineering
,” Power Engineering Society Summer Meeting
, IEEE, Vol. 4
, pp. 2085
–2090
.32.
Orsagh
, R. F.
, Lee
, H.
, Watson
, M.
, Byington
, C. S.
, and Powers
, J.
, 2006, “Advanced Vibration Monitoring for Wind Turbine Health Management
,” Impact Technologies, http://www.impact-tek.comhttp://www.impact-tek.com33.
Mobley
, R. K.
, 2002, An Introduction to Predictive Maintenance
, Butterworth-Heinemann
, New York
.34.
Caselitz
, P.
, Giebhardt
, J.
, and Mevenkamp
, M.
, 1997, “Application of Condition Monitoring Systems in Wind Energy Convertors
,” Proceedings of the EWEC
, Dublin, pp. 1
–4
.35.
Caselitz
, P.
, Giebhardt
, J.
, and Mevenkamp
, M.
, 1996, “Development of a Fault Detection System for Wind Energy Convertors
,” Proceedings of the EUWEC
, Goteborg, pp. 1004
–1007
.36.
McGrail
, A. J.
, Gulski
, E.
, Groot
, E. R. S.
, Allan
, D.
, Birthwhistle
, D.
, and Blackburn
, T. R.
, 2002, “Data Mining Techniques to Asses the Condition of High Voltage Electrical Plant
,” CIGRE Paris WG15.11 Paper.37.
Smit
, J. J.
, 1998, “Decision Making Experience With Maintenance Diagnosis of High Voltage Equipment
,” The 37th CIGRE Session
, Paris, pp. 10
–15
.38.
Jardine
, K. S.
, Ralston
, P.
, Reid
, N.
, and Stafford
, J.
, 1989, “Proportional Hazards Analysis of Diesel Engine Failure Data
,” Qual. Reliab. Eng. Int
0748-8017, 5
(3
), pp. 207
–216
.39.
Romanowski
, C. J.
, and Nagi
, R.
, 2001, “Analyzing Maintenance Data Using Data Mining Methods
,” Data Mining for Design and Manufacturing: Methods and Applications
, D.
Braha
, ed., Kluwer Academic
, Dordrecht
, pp. 235
–254
.40.
Raheja
, D.
, Llinas
, J.
, Nagi
, R.
, and Romanowski
, C.
, 2006, “Data Fusion/Data Mining-Based Architecture for Condition-Based Maintenance
,” Int. J. Prod. Res.
0020-7543, 44
(14
), pp. 2869
–2887
.41.
Agrawal
, R.
, and Srikant
, R.
, 1994, “Fast Algorithms for Mining Association Rules in Large Databases
,” The 20th International Conference on Very Large Databases
, Santiago, Chile, pp. 478
–499
.42.
Cho
, Y. H.
, Kim
, J. K.
, and Kim
, S. H.
, 2002, “A Personalized Recommender System Based on Web Usage Mining and Decision Tree Induction
,” Expert Sys. Applic.
0957-4174, 23
(3
), pp. 329
–342
.43.
Luo
, J.
, and Bridges
, S. M.
, 2000, “Mining Fuzzy Association Rules and Fuzzy Frequency Episodes for Intrusion Detection
,” Int. J. Intell. Syst.
0884-8173, 15
(1
), pp. 1
–36
.44.
Besemann
, C.
, Denton
, A.
, Yekkirala
, A.
, Hutchison
, R.
, and Anderson
, M.
, 2004, “Differential Association Rule Mining for the Study of Protein-Protein Interaction Networks
,” Proceedings of the Fourth Workshop on Data Mining in Bioinformatics at SIGKDD
, pp. 72
–80
.45.
Agrawal
, R.
, Imielinski
, T.
, and Swami
, A. N.
, 1993, “Mining Association Rules Between Sets of Items in Large Databases
,” SIGMOD
, 22
(2
), pp. 207
–216
.46.
Margineantu
, D.
, 2000, “On Class Probability Estimates and Cost-Sensitive Evaluation of Classifiers
,” Workshop on Cost-Sensitive Learning at the 17th International Conference on Machine Learning
.47.
Chawla
, N. V.
, Bowyer
, K. W.
, Hall
, L. O.
, and Kegelmeyer
, W. P.
, 2002, “SMOTE: Synthetic Minority Over-Sampling Technique
,” J. Artif. Intell. Res.
1076-9757, 16
, pp. 321
–357
.48.
Smola
, A. J.
, and Scholkopf
, B.
, 2004, “A Tutorial on Support Vector Regression
,” Stat. Comput.
0960-3174, 14
(3
), pp. 199
–222
.49.
Jollife
, I. T.
, 1986, Principal Component Analysis
, Springer-Verlag
, New York
.50.
Espinosa
, J.
, Vandewalle
, J.
, and Wertz
, V.
, 2005, Fuzzy Logic, Identification and Predictive Control
, Springer-Verlag
, London
.51.
Miranda
, A. A.
, Borgne
, Y. A. L.
, and Bontempi
, G.
, 2008, “New Routes From Minimal Approximation Error to Principal Components
,” Neural Processing Letters, Springer
, 27
(3
), pp. 197
–207
.52.
Breiman
, L.
, 1996, “Bagging Predictors
,” Mach. Learn.
0885-6125, 24
(2
), pp. 123
–140
.53.
Cohen
, W.
, 1996, “Learning Trees and Rules With Set-Valued Features
,” Proceedings of the 13th Artificial Intelligence Conference
, pp. 1
–8
.54.
Rodriguez
, J. J.
, Kuncheva
, L. I.
, and Alonso
, C. J.
, 2006, “Rotation Forest: A New Classifier Ensemble Method
,” IEEE Trans. Pattern Anal. Mach. Intell.
0162-8828, 28
(10
), pp. 1619
–1630
.55.
56.
Aha
, D.
, and Kibler
, D.
, 1991, “Instance-Based Learning Algorithms
,” Mach. Learn.
0885-6125, 6
(1
), pp. 37
–66
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