M. A. Fremmelev, P. Ladpli, E. Orlowitz, N. Dervilis, M. McGugan, K. Branner
The present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.
{"title":"Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations","authors":"M. A. Fremmelev, P. Ladpli, E. Orlowitz, N. Dervilis, M. McGugan, K. Branner","doi":"10.1002/we.2854","DOIUrl":"https://doi.org/10.1002/we.2854","url":null,"abstract":"The present work investigates the performance of different features, extracted from vibration‐based data, for structural health monitoring of a 52‐meter wind turbine blade during fatigue testing. An active vibration monitoring system was used during the test campaign, providing periodic excitation of single frequencies in the medium‐frequency range, and using accelerometers to measure the vibration output on different parts of the blade. Based on previous work from the authors, data is available for the wind turbine blade in healthy state, with a manually induced damage, and with progressively increasing damage severity. Using the vibration data, different signal processing methods are used to extract damage‐sensitive features. Time series methods and time‐frequency domain methods are used to quantify the applied active vibration signal. Using outlier analysis, the health state of the blade is classified, and the classification accuracy through use of the different features is compared. Highest performance is generally obtained by auto‐regressive modeling of the vibration outputs, using the auto‐regressive parameters as features. Finally, suggestions for future improvements of the present method toward practical implementation are given.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45837029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.
{"title":"Convolutional neural network framework for wind turbine electromechanical fault detection","authors":"Emilie Stone, S. Giani, D. Zappalá, C. Crabtree","doi":"10.1002/we.2857","DOIUrl":"https://doi.org/10.1002/we.2857","url":null,"abstract":"Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47376632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{ The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre-computed quasi-steady aerodynamic mapping, which describes the relationship between pitch angle and tip-speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterised in terms of axial induction factors. Based on this insight and standard turbine measurements, real-time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine
{"title":"Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation","authors":"A. Lio, F. Meng, G. Larsen","doi":"10.1002/we.2858","DOIUrl":"https://doi.org/10.1002/we.2858","url":null,"abstract":"{ The use of state estimation techniques offers a means of inferring rotor effective wind speed from standard measurements of wind turbines. Typical wind speed estimators rely upon a pre-computed quasi-steady aerodynamic mapping, which describes the relationship between pitch angle and tip-speed ratio and the power coefficient. In practice, the static mapping does not capture the influence of turbine structural dynamics and atmospheric turbulence, inevitably resulting in poor performance of the wind speed estimation. In addition, the turbine aerodynamic properties might not be easily accessible. Thus, this paper presents a rotor effective wind speed estimation method that obviates the requirement for prior knowledge of turbine power coefficients. Specifically, the proposed method exploits a simple actuator disc model, where the aerodynamic power and thrust coefficients can be characterised in terms of axial induction factors. Based on this insight and standard turbine measurements, real-time estimation of rotor effective wind speed and axial induction factors can then be achieved using a simplified turbine","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46301167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Odeh, Kazi Mohsin, Tri D. Ngo, D. Zalkind, J. Jonkman, A. Wright, A. Robertson, Tuhin Das
{"title":"Development of a wind turbine model and simulation platform using an acausal approach: Multiphysics modeling, validation, and control","authors":"Mohammad Odeh, Kazi Mohsin, Tri D. Ngo, D. Zalkind, J. Jonkman, A. Wright, A. Robertson, Tuhin Das","doi":"10.1002/we.2853","DOIUrl":"https://doi.org/10.1002/we.2853","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45239896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Taschner, M. Folkersma, Luis A Martínez‐Tossas, R. Verzijlbergh, J. van Wingerden
{"title":"A new coupling of a GPU‐resident large‐eddy simulation code with a multiphysics wind turbine simulation tool","authors":"E. Taschner, M. Folkersma, Luis A Martínez‐Tossas, R. Verzijlbergh, J. van Wingerden","doi":"10.1002/we.2844","DOIUrl":"https://doi.org/10.1002/we.2844","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48767954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaosheng Peng, Zimin Yang, Yinhuan Li, Bo Wang, Jianfeng Che
{"title":"Short‐term wind power prediction based on stacked denoised auto‐encoder deep learning and multi‐level transfer learning","authors":"Xiaosheng Peng, Zimin Yang, Yinhuan Li, Bo Wang, Jianfeng Che","doi":"10.1002/we.2856","DOIUrl":"https://doi.org/10.1002/we.2856","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49201646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aemilius A. W. van Vondelen, A. Iliopoulos, S. Navalkar, D. van der Hoek, J. van Wingerden
{"title":"Modal analysis of an operational offshore wind turbine using enhanced Kalman filter‐based subspace identification","authors":"Aemilius A. W. van Vondelen, A. Iliopoulos, S. Navalkar, D. van der Hoek, J. van Wingerden","doi":"10.1002/we.2849","DOIUrl":"https://doi.org/10.1002/we.2849","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49398054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The flow in the induction and entrance regions of lab‐scale wind farms","authors":"M. K. Vinnes, N. Worth, A. Segalini, R. J. Hearst","doi":"10.1002/we.2855","DOIUrl":"https://doi.org/10.1002/we.2855","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43160316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares","authors":"J. Liew, T. Göçmen, W. Lio, G. Larsen","doi":"10.1002/we.2852","DOIUrl":"https://doi.org/10.1002/we.2852","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45042312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fraser Anderson, R. Dawid, D. McMillan, David García‐Cava
{"title":"A Bayesian reliability analysis exploring the effect of scheduled maintenance on wind turbine time to failure","authors":"Fraser Anderson, R. Dawid, D. McMillan, David García‐Cava","doi":"10.1002/we.2846","DOIUrl":"https://doi.org/10.1002/we.2846","url":null,"abstract":"","PeriodicalId":23689,"journal":{"name":"Wind Energy","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49401662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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