03776nam 22006615 450 991101587490332120250712073513.0981-9667-63-110.1007/978-981-96-6763-5(MiAaPQ)EBC32201085(Au-PeEL)EBL32201085(CKB)39614954100041(OCoLC)1527722714(DE-He213)978-981-96-6763-5(EXLCZ)993961495410004120250708d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputational Methods for Blade Icing Detection of Wind Turbines /by Xu Cheng, Fan Shi, Xiufeng Liu, Shengyong Chen1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (347 pages)Engineering Applications of Computational Methods,2662-3374 ;24981-9667-62-3 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.Engineering Applications of Computational Methods,2662-3374 ;24MechatronicsRenewable energy sourcesTime-series analysisMachine learningMechatronicsRenewable EnergyTime Series AnalysisMachine LearningMechatronics.Renewable energy sources.Time-series analysis.Machine learning.Mechatronics.Renewable Energy.Time Series Analysis.Machine Learning.629.8Cheng Xu1832372Shi Fan1722557Liu Xiufeng1380279Chen Shengyong1832373MiAaPQMiAaPQMiAaPQBOOK9911015874903321Computational Methods for Blade Icing Detection of Wind Turbines4406496UNINA