Predicting fan blade icing by using particle swarm optimization and support vector machine algorithm. (October 2020)

• Record Type: Journal Article
• Authors: Xu, Jiaohui; Tan, Wen; Li, Tingshun
• Is Part Of: Computers & electrical engineering. Volume 87(2020)
• Publication Date: 2020-10
• Subjects: Blade icing prediction -- Unbalanced data -- Particle swarm optimization -- Support vector machine; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106751
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Balancing the blade icing data and no icing data. Get the characteristic quantity related to the blade icing. Getting the optimal parameter values by using particle swarm optimization algorithm. Abstract: 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 undersampling 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. Graphical abstracts: Image, graphical abstract