Fault Detection of Wind Turbine Pitch System Based on Multiclass Optimal Margin Distribution Machine

• Published in: Mathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Wu, Huawei, Zhao, Qi, Kuang, Zijie, Tang, Mingzhu, Yang, Xu
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Complexity; Correlation analysis; Correlation coefficients; Efficiency; Engineering; Fault detection; Fault diagnosis; Feature selection; Hydraulics; Model testing; Parameters; Sample variance; Supervisory control and data acquisition; Support vector machines; Training; Turbines; Turbogenerators; Wavelet transforms; Wind farms; Wind power; Wind turbines
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2020/2091382

In response to the unbalanced sample categories and complex sample distribution of the operating data of the pitch system of the wind turbine generator system, this paper proposes a method for fault detection of the pitch system of the wind turbine generator system based on the multiclass optimal margin distribution machine. In this method, the power output of the wind turbine generator system is used as the main status parameter, and the operating data history of the wind turbine generator system in the wind power supervisory control and data acquisition (SCADA) system is subject to correlation analysis with the Pearson correlation coefficient, to eliminate the features that have low correlation with the power output status parameter. Secondary analysis is performed to the remaining features, thus reducing the number and complexity of samples. Datasets are divided into the training set for training of the multiclass optimal margin distribution machine fault detection model and test set for testing. Experimental verification was carried out with the operating data of one wind farm in China. Experimental results show that, compared with other support vector machines, the proposed method has higher fault detection accuracy and precision and lower false-negative rate and false-positive rate.