{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:59:24Z","timestamp":1771048764459,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T00:00:00Z","timestamp":1725321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wind turbine blades bear the maximum cyclic load and varying self-weights in turbulent wind environments, which accelerate the propagation of cracks that ultimately progress from minor faults, resulting in blade failure and significant maintenance and shutdown costs. To address this issue, this paper proposes an adaptive control strategy for the blade\u2019s useful life. The control system is divided into the inner control loop and the outer control loop. The outer loop is based on the Paris crack propagation model combined with a particle filtering algorithm and calculates the degradation of the blade life under the crack threshold conditions provided by the operation and maintenance strategy to determine the parameter settings of the inner-loop load-shedding controller. The control strategy we propose can balance the load-shedding capability of the controller with the fatigue load of the pitch actuator while considering the predefined remaining useful blade life in the operation and maintenance strategy, avoiding unplanned downtime and reducing maintenance costs.<\/jats:p>","DOI":"10.3390\/s24175729","type":"journal-article","created":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T08:38:47Z","timestamp":1725352727000},"page":"5729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on Integrated Control Strategy for Wind Turbine Blade Life"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1314-237X","authenticated-orcid":false,"given":"Bairen","family":"An","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3873-7465","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6364-0557","authenticated-orcid":false,"given":"Zeqiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1016\/j.rser.2017.06.052","article-title":"Photovoltaics and wind status in the European union after the Paris agreement","volume":"81","author":"Arantegui","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/JPROC.2015.2414485","article-title":"Nuclear Power Versus Renewable Energy\u2014A Trend Analysis [Point of View]","volume":"103","author":"Froggatt","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhuqiao, M., Zheyu, R., Tongguang, G., Zewen, Y., Yijie, H., and Yeqi, F. (2020, January 15\u201317). Design of fatigue endurance experiment method for automotive torsion beam based on rain-flow method. Proceedings of the 2020 2nd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM), Manchester, UK.","DOI":"10.1109\/AIAM50918.2020.00095"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TMECH.2022.3215545","article-title":"Bearing Weak Fault Feature Extraction Under Time-Varying Speed Conditions Based on Frequency Matching Demodulation Transform","volume":"28","author":"Zhao","year":"2023","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109124","DOI":"10.1016\/j.ress.2023.109124","article-title":"Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines","volume":"233","author":"Li","year":"2023","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111112","DOI":"10.1016\/j.ymssp.2024.111112","article-title":"Frequency-chirprate synchro squeezing-based scaling chirplet transform for wind turbine nonstationary fault feature time\u2013frequency representation","volume":"209","author":"Zhao","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108999","DOI":"10.1016\/j.cie.2023.108999","article-title":"A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model","volume":"176","author":"Xu","year":"2023","journal-title":"Comput. Ind. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"110190","DOI":"10.1016\/j.ymssp.2023.110190","article-title":"Complex domain extension network with multi-channels information fusion for remaining useful life prediction of rotating machinery","volume":"192","author":"Cao","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Thomas, M., Boettcher, N., Huish, C., Shakya, P., Seibi, A.C., Arias, D., Shekaramiz, M., and Masoum, M. (2023, January 16\u201318). Prediction of Wind Turbine Blade Fatigue and Life Using the National Renewable Energy Laboratory Open-Source Software. Proceedings of the 2023 14th International Renewable Energy Congress (IREC), Sousse, Tunisia.","DOI":"10.1109\/IREC59750.2023.10389605"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bi, J., Jiao, J., Ma, H., Ge, X., Wang, G., and Zhou, D. (2023, January 20\u201323). Prediction of Wind Turbine Blade Fatigue Life Based on GA-BP Neural Network. Proceedings of the 2023 5th International Conference on System Reliability and Safety Engineering (SRSE), Beijing, China.","DOI":"10.1109\/SRSE59585.2023.10336091"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102388","DOI":"10.1016\/j.rineng.2024.102388","article-title":"Fluid-structure interaction and life prediction of small-scale damaged horizontal axis wind turbine blades","volume":"23","author":"Shakya","year":"2024","journal-title":"Results Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7674","DOI":"10.1016\/j.ifacol.2023.10.1168","article-title":"Periodic LQG Wind Turbine Control with Adaptive Load Reduction","volume":"56","author":"Thiele","year":"2023","journal-title":"IFAC-Pap. OnLine"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tao, H. (2023, January 29\u201330). Research on Optimization Control Strategy Using Model Predictive Control of Wind Turbine Generators. Proceedings of the 2023 IEEE Sustainable Power and Energy Conference (iSPEC), Chongqing, China.","DOI":"10.1109\/iSPEC58282.2023.10402942"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"116054","DOI":"10.1016\/j.oceaneng.2023.116054","article-title":"A model reference adaptive control framework for floating offshore wind turbines with collective and individual blade pitch strategy","volume":"291","author":"Zhou","year":"2024","journal-title":"Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, N., Wright, A.D., and Johnson, K.E. (2016, January 6\u20138). Independent blade pitch controller design for a three-bladed turbine using disturbance accommodating control. Proceedings of the 2016 American Control Conference (ACC), Boston, MA, USA.","DOI":"10.1109\/ACC.2016.7525261"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2696","DOI":"10.1016\/j.asr.2020.09.008","article-title":"Sensitivity of a physically realizable heliogyro root pitch control system to inherent damping models","volume":"67","author":"Cook","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.renene.2023.05.087","article-title":"Applications of K-nearest neighbor algorithm in intelligent diagnosis of wind turbine blades damage","volume":"212","author":"Tang","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"120152","DOI":"10.1016\/j.renene.2024.120152","article-title":"Study on crack monitoring method of wind turbine blade based on AI model: Integration of classification, detection, segmentation and fault level evaluation","volume":"224","author":"Hang","year":"2024","journal-title":"Renew. Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.32604\/ee.2023.040743","article-title":"Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade","volume":"120","author":"Tang","year":"2023","journal-title":"Energy Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100366","DOI":"10.1016\/j.egyai.2024.100366","article-title":"Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection","volume":"16","author":"Rizk","year":"2024","journal-title":"Energy AI"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108443","DOI":"10.1016\/j.ijepes.2022.108443","article-title":"Adaptive active fault-tolerant MPPT control of variable-speed wind turbine considering generator actuator failure","volume":"143","author":"Chen","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2544","DOI":"10.1016\/j.matpr.2020.11.259","article-title":"Fuzzy logic based optimal tip speed ratio MPPT controller for grid connected WECS","volume":"45","author":"Babu","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1016\/j.enconman.2014.06.055","article-title":"Modeling, analysis and comparison of TSR and OTC methods for MPPT and power smoothing in permanent magnet synchronous generator-based wind turbines","volume":"86","author":"Nasiri","year":"2014","journal-title":"Energy Convers. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.weer.2024.100001","article-title":"Wavelet LQR based gain scheduling for power regulation and vibration control of floating horizontal axis wind turbine with double-pitched rotor","volume":"1","author":"Mitra","year":"2024","journal-title":"Wind. Energy Eng. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111048","DOI":"10.1016\/j.ymssp.2023.111048","article-title":"Adaptive Bayesian filter with data-driven sparse state space model for seismic response estimation","volume":"208","author":"Kitahara","year":"2024","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.promfg.2019.02.286","article-title":"Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco","volume":"32","author":"Bidaoui","year":"2019","journal-title":"Procedia Manuf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5729\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:47:58Z","timestamp":1760111278000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5729"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,3]]},"references-count":26,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175729"],"URL":"https:\/\/doi.org\/10.3390\/s24175729","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,3]]}}}