Abstract
The operation process of the wind turbine generator is complex, the running state variables are many, and the variables are related to each other. It is difficult to achieve the expected results if the traditional fault diagnosis method is used. The fault diagnosis method based on principal component analysis (PCA) of the feature extraction of wind turbine generator is presented in this paper. The principal component model is established based on the normal working condition history data at first, and the control limits of Hotelling \( T^{2} \) and SPE two statistics are obtained. The condition monitoring and fault location of generating sets are realized by comparing the statistics of real-time operation and the size of the threshold. The experimental simulation results of the operation data of the wind turbine generator show the effectiveness of the method.
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Acknowledgments
This work is supported by National Natural Science Foundation of China under Grant (60974063, 61175059), and the Nature Science Foundation of Hebei under Contract (F2014205115), and the Education Department Project of Hebei under Contract (No: ZD2016053).
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© 2016 Springer Science+Business Media Singapore
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Lv, F., He, J., Zhang, Z., Li, L., Ju, X. (2016). Fault Diagnosis Method of Wind Turbine Generators Based on Principal Component Feature Extraction. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_8
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DOI: https://doi.org/10.1007/978-981-10-2338-5_8
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