Abstract
A fault detection method bases on wind turbines (WTs) supervisory control and data acquisition (SCADA) is proposed, principal component analysis (PCA) was used to reduce the dimension of target features to 1-D, so that PCA output 1-D data can be used as label of support vector machine (SVM). Thus on the premise of not losing the prediction correctness, one model can detect the fault of 2 to 4 features, largely reduce the complexity of model building. Different experiments are present to show the effectiveness of the proposed method.
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Acknowledgments
This paper is supported by Renewable Energy Research Center of China Electric Power Research Institute of STATE GRID’s science and technology project: Research on Key Technologies of condition monitoring and intelligent early detection of wind turbine based on big data.
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Qin, S., Wang, K., Ma, X., Wang, W., Li, M. (2017). An Improved SVM Based Wind Turbine Multi-fault Detection Method. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_3
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DOI: https://doi.org/10.1007/978-981-10-6385-5_3
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