.png){kind=icon}
.png){kind=icon}
Return
Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2020.106751.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Icing of wind turbine blades is a phenomenon that commonly occurs in autumn and winter and critically affects the safety and efficiency of wind turbine operation. Therefore, it is of considerable significance to predict whether wind turbine blades are frozen. The data used in this work are obtained from a supervisory control and data acquisition system. The particle swarm optimization algorithm is used to optimize the kernel function of the support vector machine to establish a model to predict whether a fan blade is frozen. Specifically, first, the data are preprocessed to eliminate apparent ice free data, and the data sets are further balanced using under-sampling and oversampling techniques. Second, the appropriate eigenvalues are selected according to the icing mechanism. The optimal parameters of the support vector machine are obtained using particle swarm optimization algorithm. Finally, the characteristic value and parameters are substituted into the support vector machine to evaluate the fault mechanism of blade icing. (C) 2020 Published by Elsevier Ltd.
Keywords:
Blade icing prediction
Unbalanced data
Particle swarm optimization
Support vector machine
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.9
Papers:
6.7K
Citations:
1.3W