Wind Energy最新文献_第5页

Feasibility study on a full‐scale wind turbine blade monitoring campaign: Comparing performance and robustness of features extracted from medium‐frequency active vibrations 全尺寸风力涡轮机叶片监测活动的可行性研究：比较从中频主动振动中提取的特征的性能和稳健性

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-08-10 DOI: 10.1002/we.2854

M. A. Fremmelev, P. Ladpli, E. Orlowitz, N. Dervilis, M. McGugan, K. Branner

{"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}

引用次数: 0

Convolutional neural network framework for wind turbine electromechanical fault detection 风电机电故障检测的卷积神经网络框架

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-08-07 DOI: 10.1002/we.2857

Emilie Stone, S. Giani, D. Zappalá, C. Crabtree

{"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}

引用次数: 1

Real‐time rotor effective wind speed estimation based on actuator disc theory: Design and full‐scale experimental validation 基于传动盘理论的实时转子有效风速估计：设计和全尺寸实验验证

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-07-29 DOI: 10.1002/we.2858

A. Lio, F. Meng, G. Larsen

引用次数: 0

Development of a wind turbine model and simulation platform using an acausal approach: Multiphysics modeling, validation, and control 使用辅助方法开发风力涡轮机模型和模拟平台：多物理建模、验证和控制

IF 4.1 3区工程技术

Wind Energy Pub Date : 2023-07-16 DOI: 10.1002/we.2853

Mohammad Odeh, Kazi Mohsin, Tri D. Ngo, D. Zalkind, J. Jonkman, A. Wright, A. Robertson, Tuhin Das

引用次数: 0

A new coupling of a GPU‐resident large‐eddy simulation code with a multiphysics wind turbine simulation tool 一种新的GPU驻留大涡模拟代码与多物理场风力机仿真工具的耦合