scholarly journals Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 13078-13091 ◽  
Author(s):  
Zexian Sun ◽  
Hexu Sun
2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Thanh Toan Tran ◽  
Dong-Hyun Kim ◽  
Ba Hieu Nguyen

The accurate prediction of unsteady aerodynamic performance and loads, for floating offshore wind turbines (FOWTs), is still questionable because several conventional methods widely used for this purpose are applied in ways that violate the theoretical assumptions of their original formulation. The major objective of the present study is to investigate the unsteady aerodynamic effects for the rotating blade due to the periodic surge motions of an FOWT. This work was conducted using several numerical approaches, particularly unsteady computational fluid dynamics (CFD) with an overset grid-based approach. The unsteady aerodynamic effects that occur when an FOWT is subjected to the surge motion of its floating support platform is assumed as a sinusoidal function. The present CFD simulation based on an overset grid approach provides a sophisticated numerical model on complex flows around the rotating blades simultaneously having the platform surge motion. In addition, an in-house unsteady blade element momentum (UBEM) and the fast (fatigue, aerodynamic, structure, and turbulence) codes are also applied as conventional approaches. The unsteady aerodynamic performances and loads of the rotating blade are shown to be changed considerably depending on the amplitude and frequency of the platform surge motion. The results for the flow interaction phenomena between the oscillating motions of the rotating wind turbine blades and the generated blade-tip vortices are presented and investigated in detail.


Author(s):  
Xujie Zhang ◽  
Ping Wu ◽  
Jiajun He ◽  
Yichao Liu ◽  
Lin Wang ◽  
...  

Currently, the offshore wind turbine has become a hot research area in the wind energy industry. Among different offshore wind turbines, floating offshore wind turbine (FOWT) can harvest abundant wind energy in deepwater areas. However, the harsh working environment will dramatically increase the maintenance cost and downtime of FOWTs. Wind turbine fault diagnosis is being regarded as an indispensable system for maintenance issues. Owing to the complexity of FOWT, it imposes an enormous challenge for effective fault diagnosis. This paper develops a novel FOWT fault diagnosis method based on a stacked denoising autoencoder (SDAE). First, a sliding window technique is adopted for time-series data to preserve temporal information. Then, SDAE is employed to extract the features from high-dimensional data. Based on the extracted features from SDAE, a classifier using multilayer perceptrons (MLP) is developed to determine the health status of the FOWT. To verify the performance of the proposed method, a FOWT simulation benchmark based on the National Renewable Energy Laboratory (NREL) FAST simulator is employed. Results show the superior performance of the proposed method by comparison with other relevant methods.


2020 ◽  
Vol 6 ◽  
pp. 886-895
Author(s):  
Hailiang Xu ◽  
Zhi Li ◽  
Han Wu ◽  
Rende Zhao ◽  
Jiabing Hu

Author(s):  
Shashi Mehrotra ◽  
Shruti Kohli

It is needed to organize the data in different groups for various purposes, where clustering is useful. The chapter covers Data Clustering in the detail, which includes; introduction to data clustering with figures, data clustering process, basic classification of clustering and applications of clustering, describing hard partition clustering and fuzzy clustering. Some most commonly used clustering method are explained in the chapter with their features, advantages, and disadvantages. A various variant of K-Means and extension method of hierarchical clustering method, density-based clustering method and grid-based clustering method are covered.


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