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Computational methods for blade icin...

Cheng, Xu.

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Computational methods for blade icing detection of wind turbines

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational methods for blade icing detection of wind turbines/ by Xu Cheng ... [et al.].
其他作者:	Cheng, Xu.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xiii, 229 p. :ill., digital ;24 cm.
內容註:	Introduction -- State of the art -- Modeling of time series -- Attention-based convolutional neural network for blade icing detection -- Multiscale Graph-based neural network for blade icing detection -- Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection -- Prototype-based Semi-supervised blade icing detection -- Class Imbalanced Federated Learning Model for Blade Icing Detection -- Heterogeneous Federated Learning Model for Blade Icing Detection -- Blockchain-enhanced Federated Learning Model for Blade Icing Detection -- Concluding remarks.
Contained By:	Springer Nature eBook
標題:	Wind turbines. -
電子資源:	https://doi.org/10.1007/978-981-96-6763-5
ISBN:	9789819667635

Computational methods for blade icing detection of wind turbines
Computational methods for blade icing detection of wind turbines[electronic resource] /by Xu Cheng ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xiii, 229 p. :ill., digital ;24 cm. - Engineering applications of computational methods,v. 242662-3374 ;. - Engineering applications of computational methods ;volume 24..

Introduction -- State of the art -- Modeling of time series -- Attention-based convolutional neural network for blade icing detection -- Multiscale Graph-based neural network for blade icing detection -- Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection -- Prototype-based Semi-supervised blade icing detection -- Class Imbalanced Federated Learning Model for Blade Icing Detection -- Heterogeneous Federated Learning Model for Blade Icing Detection -- Blockchain-enhanced Federated Learning Model for Blade Icing Detection -- Concluding remarks.

This book thoroughly explores the realm of data-driven blade-icing detection for wind turbines, focusing on multivariate time series classification to enhance the reliability and efficiency of wind energy utilization. The widespread prevalence of sensor technology in wind turbines, coupled with substantial data collection, has paved the way for advanced data-driven methodologies, which do not require extensive domain knowledge or additional mechanical tools. The interdisciplinary appeal of this study has drawn attention from experts in fields like computer science, mechanical engineering, and renewable energy systems. Adopting a comprehensive approach, the book lays down a foundational framework for blade-icing detection, stressing the critical role of sensor data integration and the profound impact of machine learning techniques in refining the detection processes. The book is designed for undergraduate and graduate students keen on renewable energy technologies, researchers delving into machine learning applications in energy systems, and engineers focusing on sustainable solutions for enhancing wind turbine performance.

ISBN: 9789819667635

Standard No.: 10.1007/978-981-96-6763-5doiSubjects--Topical Terms:

672559
Wind turbines.

LC Class. No.: TJ828 / .C44 2025

Dewey Class. No.: 621.45

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505 0 $a Introduction -- State of the art -- Modeling of time series -- Attention-based convolutional neural network for blade icing detection -- Multiscale Graph-based neural network for blade icing detection -- Multiscale Wavelet-Driven Graph Convolutional Network for Blade Icing Detection -- Prototype-based Semi-supervised blade icing detection -- Class Imbalanced Federated Learning Model for Blade Icing Detection -- Heterogeneous Federated Learning Model for Blade Icing Detection -- Blockchain-enhanced Federated Learning Model for Blade Icing Detection -- Concluding remarks.
520 $a This book thoroughly explores the realm of data-driven blade-icing detection for wind turbines, focusing on multivariate time series classification to enhance the reliability and efficiency of wind energy utilization. The widespread prevalence of sensor technology in wind turbines, coupled with substantial data collection, has paved the way for advanced data-driven methodologies, which do not require extensive domain knowledge or additional mechanical tools. The interdisciplinary appeal of this study has drawn attention from experts in fields like computer science, mechanical engineering, and renewable energy systems. Adopting a comprehensive approach, the book lays down a foundational framework for blade-icing detection, stressing the critical role of sensor data integration and the profound impact of machine learning techniques in refining the detection processes. The book is designed for undergraduate and graduate students keen on renewable energy technologies, researchers delving into machine learning applications in energy systems, and engineers focusing on sustainable solutions for enhancing wind turbine performance.
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