Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine Learning Article Swipe
Zuojun Liu
,
Cheng Xiao
,
Tieling Zhang
,
Xu Zhang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/en13020460
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/en13020460
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.
Related Topics
Concepts
SCADA
Fault detection and isolation
Turbine
Support vector machine
Fault (geology)
Wind power
Artificial neural network
Computer science
Fault indicator
Real-time computing
Convolutional neural network
Reliability engineering
Engineering
Control engineering
Data mining
Artificial intelligence
Seismology
Actuator
Electrical engineering
Mechanical engineering
Geology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en13020460
- https://www.mdpi.com/1996-1073/13/2/460/pdf?version=1579400638
- OA Status
- gold
- Cited By
- 44
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3000155135
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3000155135Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en13020460Digital Object Identifier
- Title
-
Research on Fault Detection for Three Types of Wind Turbine Subsystems Using Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-17Full publication date if available
- Authors
-
Zuojun Liu, Cheng Xiao, Tieling Zhang, Xu ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/en13020460Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/13/2/460/pdf?version=1579400638Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/13/2/460/pdf?version=1579400638Direct OA link when available
- Concepts
-
SCADA, Fault detection and isolation, Turbine, Support vector machine, Fault (geology), Wind power, Artificial neural network, Computer science, Fault indicator, Real-time computing, Convolutional neural network, Reliability engineering, Engineering, Control engineering, Data mining, Artificial intelligence, Seismology, Actuator, Electrical engineering, Mechanical engineering, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
44Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 12, 2023: 7, 2022: 9, 2021: 7Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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