The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

The Use of an Improved LSSVM and Joint Normalization on Temperature Prediction of Gearbox Output Shaft in DFWT

In the working process of Double-Fed Wind Turbines (DFWT), it is very important to monitor and predict the temperature of the high-speed output shaft of the gearbox timely and effectively. Support vector machine has more advantages in the temperature prediction of wind turbines. Least squares suppor...

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Bibliographic Details
Main Authors: Yancai Xiao, Ruolan Dai, Guangjian Zhang, Weijia Chen
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Energies
Subjects:
temperature prediction
least squares support vector machine (LSSVM)
pruning algorithm
joint normalization
principal component analysis (PCA)
particle swarm optimization (PSO)
Online Access:https://www.mdpi.com/1996-1073/10/11/1877

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https://www.mdpi.com/1996-1073/10/11/1877

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