Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning

Zuojun Liu, Cheng Xiao, Tieling Zhang and Xu Zhang
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Zuojun Liu: School of Control Science and Engineering, Hebei University of Technology, Tianjin 300131, China
Cheng Xiao: School of Control Science and Engineering, Hebei University of Technology, Tianjin 300131, China
Tieling Zhang: Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia
Xu Zhang: Department of Technical Development, AT&M Environmental Engineering Technology Co., Ltd., Beijing 100801, China

Energies, 2020, vol. 13, issue 2, 1-21

Abstract: In wind power generation, one aim of wind turbine control is to maintain it in a safe operational status while achieving cost-effective operation. The purpose of this paper is to investigate new techniques for wind turbine fault detection based on supervisory control and data acquisition (SCADA) system data in order to avoid unscheduled shutdowns. The proposed method starts with analyzing and determining the fault indicators corresponding to a failure mode. Three main system failures including generator failure, converter failure and pitch system failure are studied. First, the indicators data corresponding to each of the three key failures are extracted from the SCADA system, and the radar charts are generated. Secondly, the convolutional neural network with ResNet50 as the backbone network is selected, and the fault model is trained using the radar charts to detect the fault and calculate the detection evaluation indices. Thirdly, the support vector machine classifier is trained using the support vector machine method to achieve fault detection. In order to show the effectiveness of the proposed radar chart-based methods, support vector regression analysis is also employed to build the fault detection model. By analyzing and comparing the fault detection accuracy among these three methods, it is found that the fault detection accuracy by the models developed using the convolutional neural network is obviously higher than the other two methods applied given the same data condition. Therefore, the newly proposed method for wind turbine fault detection is proved to be more effective.

Keywords: fault detection; radar chart; wind turbine generator; converter; wind turbine pitch system; convolutional neural network; support vector machine (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 (4)

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