{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T11:22:43Z","timestamp":1776165763094,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jeju National University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind turbines are widely used worldwide to generate clean, renewable energy. The biggest issue with a wind turbine is reducing failures and downtime, which lowers costs associated with operations and maintenance. Wind turbines\u2019 consistency and timely maintenance can enhance their performance and dependability. Still, the traditional routine configuration makes detecting faults of wind turbines difficult. Supervisory control and data acquisition (SCADA) produces reliable and affordable quality data for the health condition of wind turbine operations. For wind power to be sufficiently reliable, it is crucial to retrieve useful information from SCADA successfully. This article proposes a new AdaBoost, K-nearest neighbors, and logistic regression-based stacking ensemble (AKL-SE) classifier to classify the faults of the wind turbine condition monitoring system. A stacking ensemble classifier integrates different classification models to enhance the model\u2019s accuracy. We have used three classifiers, AdaBoost, K-nearest neighbors, and logistic regression, as base models to make output. The output of these three classifiers is used as input in the logistic regression classifier\u2019s meta-model. To improve the data validity, SCADA data are first preprocessed by cleaning and removing any abnormal data. Next, the Pearson correlation coefficient was used to choose the input variables. The Stacking Ensemble classifier was trained using these parameters. The analysis demonstrates that the suggested method successfully identifies faults in wind turbines when applied to local 3 MW wind turbines. The proposed approach shows the potential for effective wind energy use, which could encourage the use of clean energy.<\/jats:p>","DOI":"10.3390\/s22186955","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"6955","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2561-4389","authenticated-orcid":false,"given":"Prince","family":"Waqas Khan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1107-9941","authenticated-orcid":false,"given":"Yung-Cheol","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xiang, L., Yang, X., Hu, A., Su, H., and Wang, P. (2022). Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks. Appl. Energy, 305.","DOI":"10.1016\/j.apenergy.2021.117925"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.renene.2022.02.061","article-title":"Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning","volume":"189","author":"Zhu","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.jclepro.2018.05.126","article-title":"An anomaly identification model for wind turbine state parameters","volume":"195","author":"Zhang","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_4","first-page":"1893","article-title":"Adaptive error curve learning ensemble model for improving energy consumption forecasting","volume":"69","author":"Khan","year":"2021","journal-title":"Comput. Mater. Contin"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1049\/iet-rpg:20070044","article-title":"Overview of different wind generator systems and their comparisons","volume":"2","author":"Li","year":"2008","journal-title":"IET Renew. Power Gener."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TSTE.2018.2801625","article-title":"Wind turbine fault detection and identification through PCA-based optimal variable selection","volume":"9","author":"Wang","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1016\/j.energy.2019.03.057","article-title":"Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy","volume":"174","author":"Chen","year":"2019","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bodla, M.K., Malik, S.M., Rasheed, M.T., Numan, M., Ali, M.Z., and Brima, J.B. (2016, January 5\u20137). Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines. Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China.","DOI":"10.1109\/ICIEA.2016.7603846"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1504\/IJRET.2018.090105","article-title":"Wavelet and Hilbert Huang transform based wind turbine imbalance fault classification model using k-nearest neighbour algorithm","volume":"9","author":"Malik","year":"2018","journal-title":"Int. J. Renew. Energy Technol."},{"key":"ref_10","first-page":"022017","article-title":"Wind turbine failure prediction using SCADA data","volume":"Volume 1618","author":"Lima","year":"2020","journal-title":"Proceedings of the Journal of Physics: Conference Series"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1002\/we.2510","article-title":"Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines","volume":"23","author":"Chatterjee","year":"2020","journal-title":"Wind Energy"},{"key":"ref_12","first-page":"443","article-title":"Fault diagnosis of wind power converters based on compressed sensing theory and weight constrained Adaboost-SVM","volume":"19","author":"Zheng","year":"2019","journal-title":"J. Power Electron."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wu, Z., Wang, X., and Jiang, B. (2020). Fault diagnosis for wind turbines based on ReliefF and eXtreme gradient boosting. Appl. Sci., 10.","DOI":"10.3390\/app10093258"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.jweia.2017.06.016","article-title":"Algorithm for damage detection in wind turbine blades using a hybrid dense sensor network with feature level data fusion","volume":"168","author":"Downey","year":"2017","journal-title":"J. Wind. Eng. Ind. Aerodyn."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kushwah, K., Sahoo, S., and Joshuva, A. (2021, January 9\u201311). Health Monitoring of Wind Turbine Blades Through Vibration Signal Using Machine Learning Techniques. Proceedings of the International Conference on Computing and Communication Systems, Vellore, India.","DOI":"10.1007\/978-981-33-4084-8_22"},{"key":"ref_16","first-page":"202","article-title":"Logistic model tree classifier for condition monitoring of wind turbine blades","volume":"8","author":"Joshuva","year":"2019","journal-title":"Int. J. Recent Technol. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.renene.2012.06.013","article-title":"Wind turbine fault diagnosis method based on diagonal spectrum and clustering binary tree SVM","volume":"50","author":"Wenyi","year":"2013","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TIE.2018.2844805","article-title":"Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox","volume":"66","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.renene.2021.12.056","article-title":"Convolutional neural network fault classification based on time-series analysis for benchmark wind turbine machine","volume":"185","author":"Rahimilarki","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, X., Tee, K.F., Li, Q., and Wu, X.P. (2020). Comparative study of onshore and offshore wind characteristics and wind energy potentials: A case study for southeast coastal region of China. Sustain. Energy Technol. Assess., 39.","DOI":"10.1016\/j.seta.2020.100711"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vidal, Y., Pozo, F., and Tutiv\u00e9n, C. (2018). Wind turbine multi-fault detection and classification based on SCADA data. Energies, 11.","DOI":"10.3390\/en11113018"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Miele, E.S., Bonacina, F., and Corsini, A. (2022). Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series. Energy AI, 8.","DOI":"10.1016\/j.egyai.2022.100145"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","article-title":"Fault diagnosis of wind turbine based on Long Short-term memory networks","volume":"133","author":"Lei","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.isatra.2020.10.060","article-title":"Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals","volume":"111","author":"He","year":"2021","journal-title":"ISA Trans."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3863","DOI":"10.1016\/j.aej.2020.06.041","article-title":"An insight on VMD for diagnosing wind turbine blade faults using C4. 5 as feature selection and discriminating through multilayer perceptron","volume":"59","author":"Joshuva","year":"2020","journal-title":"Alex. Eng. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.renene.2016.03.025","article-title":"Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform","volume":"93","author":"Teng","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1002\/we.2552","article-title":"Costs of repair of wind turbine blades: Influence of technology aspects","volume":"23","author":"Mishnaevsky","year":"2020","journal-title":"Wind Energy"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ou, Y., Tatsis, K.E., Dertimanis, V.K., Spiridonakos, M.D., and Chatzi, E.N. (2021). Vibration-based monitoring of a small-scale wind turbine blade under varying climate conditions. Part I: An experimental benchmark. Struct. Control. Health Monit., 28.","DOI":"10.1002\/stc.2734"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.ymssp.2018.12.039","article-title":"Adaptive event-triggered anomaly detection in compressed vibration data","volume":"122","author":"Zhang","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Coronado, D., and Wenske, J. (2018). Monitoring the oil of wind-turbine gearboxes: Main degradation indicators and detection methods. Machines, 6.","DOI":"10.3390\/machines6020025"},{"key":"ref_31","first-page":"289","article-title":"A review of the application of oil analysis in condition monitoring and life prediction of wind turbine gearboxes","volume":"63","author":"Bie","year":"2021","journal-title":"Insight-Non-Destr. Test. Cond. Monit."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Leahy, K., Hu, R.L., Konstantakopoulos, I.C., Spanos, C.J., and Agogino, A.M. (2016, January 20\u201322). Diagnosing wind turbine faults using machine learning techniques applied to operational data. Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (Icphm), Ottawa, ON, Canada.","DOI":"10.1109\/ICPHM.2016.7542860"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Khan, P.W., Kim, Y., Byun, Y.C., and Lee, S.J. (2021). Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction. Energies, 14.","DOI":"10.3390\/en14217167"},{"key":"ref_34","unstructured":"Khan, P.W., and Byun, Y.C. Analysis of factors affecting machine learning-based energy prediction. Proceedings of the KIIT Conference."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.renene.2020.06.154","article-title":"Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data","volume":"161","author":"Pang","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"865413","DOI":"10.3389\/fenrg.2022.865413","article-title":"Optimal Photovoltaic Panel Direction and Tilt Angle Prediction Using Stacking Ensemble Learning","volume":"10","author":"Khan","year":"2022","journal-title":"Front. Energy Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lu, W., Liu, J., Fu, X., Yang, J., and Jones, S. (2022). Integrating machine learning into path analysis for quantifying behavioral pathways in bicycle-motor vehicle crashes. Accid. Anal. Prev., 168.","DOI":"10.1016\/j.aap.2022.106622"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.engappai.2015.09.011","article-title":"BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification","volume":"49","author":"Guo","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_39","unstructured":"Piech, C. (2022, May 07). Logistic Regression. Available online: https:\/\/pdfs.semanticscholar.org\/8a27\/7cf63806ee25977bb8a59fa511e5918d2cfe.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TCBB.2018.2833463","article-title":"SecureLR: Secure logistic regression model via a hybrid cryptographic protocol","volume":"16","author":"Jiang","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ni\u00f1o-Adan, I., Landa-Torres, I., Portillo, E., and Manjarres, D. (2022). Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0. Eng. Appl. Artif. Intell., 111.","DOI":"10.1016\/j.engappai.2022.104807"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Leahy, K., Hu, R.L., Konstantakopoulos, I.C., Spanos, C.J., Agogino, A.M., and O\u2019Sullivan, D.T. (2018). Diagnosing and predicting wind turbine faults from SCADA data using support vector machines. Int. J. Progn. Health Manag., 9.","DOI":"10.36001\/ijphm.2018.v9i1.2692"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yansari, R.T., Mirzarezaee, M., Sadeghi, M., and Araabi, B.N. (2022). A new survival analysis model in adjuvant Tamoxifen-treated breast cancer patients using manifold-based semi-supervised learning. J. Comput. Sci., 61.","DOI":"10.1016\/j.jocs.2022.101645"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6955\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:31:13Z","timestamp":1760142673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186955"],"URL":"https:\/\/doi.org\/10.3390\/s22186955","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]}}}