LEADER 03776nam 22006615 450 001 9911015874903321 005 20250712073513.0 010 $a981-9667-63-1 024 7 $a10.1007/978-981-96-6763-5 035 $a(MiAaPQ)EBC32201085 035 $a(Au-PeEL)EBL32201085 035 $a(CKB)39614954100041 035 $a(OCoLC)1527722714 035 $a(DE-He213)978-981-96-6763-5 035 $a(EXLCZ)9939614954100041 100 $a20250708d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Methods for Blade Icing Detection of Wind Turbines /$fby Xu Cheng, Fan Shi, Xiufeng Liu, Shengyong Chen 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (347 pages) 225 1 $aEngineering Applications of Computational Methods,$x2662-3374 ;$v24 311 08$a981-9667-62-3 327 $aIntroduction -- 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. 330 $aThis book thoroughly explores the realm of data-driven blade-icing detection for wind turbines, focusing on multivariate time series classification to enhance the reliability and efficiency of wind energy utilization. The widespread prevalence of sensor technology in wind turbines, coupled with substantial data collection, has paved the way for advanced data-driven methodologies, which do not require extensive domain knowledge or additional mechanical tools. The interdisciplinary appeal of this study has drawn attention from experts in fields like computer science, mechanical engineering, and renewable energy systems. Adopting a comprehensive approach, the book lays down a foundational framework for blade-icing detection, stressing the critical role of sensor data integration and the profound impact of machine learning techniques in refining the detection processes. The book is designed for undergraduate and graduate students keen on renewable energy technologies, researchers delving into machine learning applications in energy systems, and engineers focusing on sustainable solutions for enhancing wind turbine performance. 410 0$aEngineering Applications of Computational Methods,$x2662-3374 ;$v24 606 $aMechatronics 606 $aRenewable energy sources 606 $aTime-series analysis 606 $aMachine learning 606 $aMechatronics 606 $aRenewable Energy 606 $aTime Series Analysis 606 $aMachine Learning 615 0$aMechatronics. 615 0$aRenewable energy sources. 615 0$aTime-series analysis. 615 0$aMachine learning. 615 14$aMechatronics. 615 24$aRenewable Energy. 615 24$aTime Series Analysis. 615 24$aMachine Learning. 676 $a629.8 700 $aCheng$b Xu$01832372 701 $aShi$b Fan$01722557 701 $aLiu$b Xiufeng$01380279 701 $aChen$b Shengyong$01832373 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911015874903321 996 $aComputational Methods for Blade Icing Detection of Wind Turbines$94406496 997 $aUNINA