{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:33:51Z","timestamp":1783035231880,"version":"3.54.6"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Foundation of China","award":["82160347"],"award-info":[{"award-number":["82160347"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault alarm time lag is one of the difficulties in fault diagnosis of wind turbine generators (WTGs), and the existing methods are insufficient to achieve accurate and rapid fault diagnosis of WTGs, and the operation and maintenance costs of WTGs are too high. To invent a new method for fast and accurate fault diagnosis of WTGs, this study constructs a stacking integration model based on the machine learning algorithms light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and stochastic gradient descent regressor (SGDRegressor) using publicly available datasets from Energias De Portugal (EDP). This model is automatically tuned for hyperparameters during training using Bayesian tuning, and the coefficient of determination (R2) and root mean square error (RMSE) were used to evaluate the model to determine its applicability and accuracy. The fitted residuals of the test set were calculated, the Pauta criterion (3\u03c3) and the temporal sliding window were applied, and a final adaptive threshold method for accurate fault diagnosis and alarming was created. The model validation results show that the adaptive threshold method proposed in this study is better than the fixed threshold for diagnosis, and the alarm times for the GENERATOR fault type, GENERATOR_BEARING fault type, and TRANSFORMER fault type are 1.5 h, 5.8 h, and 3 h earlier, respectively.<\/jats:p>","DOI":"10.3390\/s23136198","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"6198","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Fault Diagnosis of Wind Turbine Generators Based on Stacking Integration Algorithm and Adaptive Threshold"],"prefix":"10.3390","volume":"23","author":[{"given":"Zhanjun","family":"Tang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobing","family":"Shi","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huayu","family":"Zou","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"},{"name":"Key Laboratory of Artificial Intelligence in Yunnan Province, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yushi","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yunnan Open University, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s41601-020-00172-w","article-title":"Improving low-voltage ride-through capability of a multimegawatt DFIG based wind turbine under grid faults","volume":"5","author":"Nadour","year":"2020","journal-title":"Prot. Control Mod. Power Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"185557","DOI":"10.1109\/ACCESS.2020.3029435","article-title":"Research on fault diagnosis of wind turbine based on SCADA data","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neucom.2022.01.067","article-title":"A fault diagnosis method for wind turbines with limited labeled data based on balanced joint adaptive network","volume":"481","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rser.2018.08.044","article-title":"Development of wind power industry in China: A comprehensive assessment","volume":"97","author":"Dai","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_5","unstructured":"Global Wind Energy Council (2023, April 10). GWEC Global Wind Report 2023. Available online: https:\/\/gwec.net\/globalwindreport2023\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.renene.2019.12.143","article-title":"Long-term visual impacts of aging infrastructure: Challenges of decommissioning wind power infrastructure and a survey of alternative strategies","volume":"150","author":"Pevzner","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108614","DOI":"10.1016\/j.apacoust.2021.108614","article-title":"Second-order Synchrosqueezing Modified S Transform for wind turbine fault diagnosis","volume":"189","author":"Yi","year":"2022","journal-title":"Appl. Acoust."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4006","DOI":"10.1049\/rpg2.12319","article-title":"Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis","volume":"15","author":"Wu","year":"2021","journal-title":"IET Renew. Power Gener."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.3390\/en12142801","article-title":"A survey of condition monitoring and fault diagnosis toward integrated O&M for wind turbines","volume":"12","author":"Zhang","year":"2019","journal-title":"Energies"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.renene.2015.12.010","article-title":"Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals","volume":"89","author":"Chen","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2226","DOI":"10.1109\/TII.2013.2243743","article-title":"From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis","volume":"9","author":"Dai","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques\u2014Part I: Fault diagnosis with model-based and signal-based approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1016\/j.rser.2019.01.013","article-title":"A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones","volume":"103","author":"Bakdi","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.renene.2019.06.103","article-title":"An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples","volume":"145","author":"Wang","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1109\/TCST.2015.2389713","article-title":"A comparative study of three fault diagnosis schemes for wind turbines","volume":"23","author":"Dey","year":"2015","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1109\/TPEL.2014.2342506","article-title":"Multiple open-circuit faults diagnosis in back-to-back converters of PMSG drives for wind turbine systems","volume":"30","author":"Jlassi","year":"2014","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.renene.2018.10.088","article-title":"Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data","volume":"134","author":"Li","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.renene.2021.10.024","article-title":"Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for multi-sensors","volume":"182","author":"Xu","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.renene.2019.11.012","article-title":"Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal","volume":"151","author":"Miao","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"053302","DOI":"10.1063\/5.0014223","article-title":"Mask-MRNet: A deep neural network for wind turbine blade fault detection","volume":"12","author":"Zhang","year":"2020","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89","DOI":"10.2478\/msr-2013-0010","article-title":"Measurement and analysis of current signals for gearbox fault recognition of wind turbine","volume":"13","author":"Lin","year":"2013","journal-title":"Meas. Sci. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107950","DOI":"10.1016\/j.measurement.2020.107950","article-title":"Reliability analysis of wind turbine blades based on non-Gaussian wind load impact competition failure model","volume":"164","author":"Zhao","year":"2020","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1109\/TMECH.2020.2978136","article-title":"An integrated feature-based failure prognosis method for wind turbine bearings","volume":"25","author":"Rezamand","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147481","DOI":"10.1109\/ACCESS.2021.3124025","article-title":"Fault diagnosis methods based on machine learning and its applications for wind turbines: A review","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gao, Z., and Liu, X. (2021). An overview on fault diagnosis, prognosis and resilient control for wind turbine systems. Processes, 9.","DOI":"10.3390\/pr9020300"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Murgia, A., Verbeke, R., Tsiporkova, E., Terzi, L., and Astolfi, D. (2023). Discussion on the Suitability of SCADA-Based Condition Monitoring for Wind Turbine Fault Diagnosis through Temperature Data Analysis. Energies, 16.","DOI":"10.3390\/en16020620"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"6875","DOI":"10.1109\/TII.2020.3041114","article-title":"A spatio-temporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced SCADA data","volume":"17","author":"He","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1016\/j.renene.2019.07.110","article-title":"Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method","volume":"145","author":"Qiu","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.renene.2021.01.143","article-title":"Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data","volume":"171","author":"Li","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_31","first-page":"102995","article-title":"A correlation-graph-CNN method for fault diagnosis of wind turbine based on state tracking and data driving model","volume":"56","author":"Wang","year":"2023","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, H., Jiang, G., Wang, Y., and Ren, S. (2020). A multiscale spatio-temporal convolutional deep belief network for sensor fault detection of wind turbine. Sensors, 20.","DOI":"10.3390\/s20123580"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.renene.2016.12.013","article-title":"Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations","volume":"116","author":"Li","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.renene.2017.09.061","article-title":"A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine","volume":"116","author":"Gao","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, G., Wang, C., Zhang, D., and Yang, G. (2021). An improved feature selection method based on random forest algorithm for wind turbine condition monitoring. Sensors, 21.","DOI":"10.3390\/s21165654"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"21020","DOI":"10.1109\/ACCESS.2018.2818678","article-title":"A data-driven design for fault detection of wind turbines using random forests and XGboost","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_38","first-page":"870","article-title":"Research on fault diagnosis of wind power generator blade based on SC-SMOTE and kNN","volume":"16","author":"Peng","year":"2020","journal-title":"J. Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Santolamazza, A., Dadi, D., and Introna, V. (2021). A data-mining approach for wind turbine fault detection based on SCADA data analysis using artificial neural networks. Energies, 14.","DOI":"10.3390\/en14071845"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"69307","DOI":"10.1109\/ACCESS.2021.3075547","article-title":"Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm","volume":"9","author":"Wumaier","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","first-page":"114","article-title":"Fault Diagnosis Method of Wind Turbine Based on Deep Belief Network","volume":"23","author":"Mengshi","year":"2019","journal-title":"J. Electr. Mach. Control"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Waqas Khan, P., and Byun, Y.C. (2022). Multi-Fault Detection and Classification of Wind Turbines Using Stacking Classifier. Sensors, 22.","DOI":"10.3390\/s22186955"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111423","DOI":"10.1016\/j.enbuild.2021.111423","article-title":"Quantitative assessments on advanced data synthesis strategies for enhancing imbalanced AHU fault diagnosis performance","volume":"252","author":"Fan","year":"2021","journal-title":"Energy Build."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the KDD \u201816: The 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101315","DOI":"10.1016\/j.uclim.2022.101315","article-title":"AI-based air quality PM2. 5 forecasting models for developing countries: A case study of Ho Chi Minh City, Vietnam","volume":"46","author":"Rakholia","year":"2022","journal-title":"Urban Clim."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Polikar, R. (2012). Ensemble Machine Learning, Springer.","DOI":"10.1007\/978-1-4419-9326-7_1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106752","DOI":"10.1016\/j.ymssp.2020.106752","article-title":"An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm","volume":"142","author":"Li","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.ress.2017.12.016","article-title":"An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction","volume":"184","author":"Li","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"024006","DOI":"10.1088\/1361-6501\/abbe3b","article-title":"Rolling bearing remaining useful life prediction via weight tracking relevance vector machine","volume":"32","author":"Tang","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1002\/tee.23247","article-title":"Transformer fault diagnosis based on stacking ensemble learning","volume":"15","author":"Wang","year":"2020","journal-title":"IEEJ Trans. Electr. Electron. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/BF00117832","article-title":"Stacked regressions","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_52","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"114203","DOI":"10.1016\/j.engstruct.2022.114203","article-title":"Neural-network based wind pressure prediction for low-rise buildings with genetic algorithm and Bayesian optimization","volume":"260","author":"Ding","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1115\/1.3653121","article-title":"A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise","volume":"86","author":"Kushner","year":"1964","journal-title":"J. Basic Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1093\/biomet\/78.3.691","article-title":"A note on a general definition of the coefficient of determination","volume":"78","author":"Nagelkerke","year":"1991","journal-title":"Biometrika"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)\u2013Arguments against avoiding RMSE in the literature","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yao, Q., Song, D., and Xu, X. (2020). Robust finger-vein ROI localization based on the 3 \u03c3 criterion dynamic threshold strategy. Sensors, 20.","DOI":"10.3390\/s20143997"},{"key":"ref_58","unstructured":"(2022, September 20). EDP OpenData. Available online: https:\/\/opendata.edp.com\/."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., and Cohen, I. (2009). Noise Reduction in Speech Processing, Springer.","DOI":"10.1007\/978-3-642-00296-0"},{"key":"ref_60","unstructured":"Myers, L., and Sirois, M.J. (2004). Encyclopedia of Statistical Sciences, Wiley."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1161\/CIRCULATIONAHA.107.654335","article-title":"Analysis of variance","volume":"117","author":"Larson","year":"2008","journal-title":"Circulation"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6198\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:07:26Z","timestamp":1760126846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6198"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,6]]},"references-count":61,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136198"],"URL":"https:\/\/doi.org\/10.3390\/s23136198","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,6]]}}}