Diagnosis of Blade Icing Using Multiple Intelligent Algorithms

Xiyun Yang, Tianze Ye, Qile Wang and Zhun Tao
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Xiyun Yang: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Tianze Ye: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Qile Wang: Zhong-Neng Power-Tech Development Co., Ltd., Beijing 100089, China
Zhun Tao: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2020, vol. 13, issue 11, 1-15

Abstract: The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

Keywords: random forest algorithm; k-nearest neighbor; fully connected neural network; blade icing recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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