Wind turbine fault detection and identification through PCA-based optimal variable selection

Wang, Yifei and Ma, Xiandong and Qian, Peng (2018) Wind turbine fault detection and identification through PCA-based optimal variable selection. IEEE Transactions on Sustainable Energy, 9 (4). pp. 1627-1635. ISSN 1949-3029

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An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. The paper firstly proposes a variable selection algorithm based on principal component analysis (PCA) with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, the paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, SCADA (Supervisory control and data acquisition) data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.

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Journal Article
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IEEE Transactions on Sustainable Energy
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08 Feb 2018 16:42
Last Modified:
21 Sep 2023 02:21