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- W3003306007 abstract "In this study, dimensionality reduction and feature extraction techniques are applied to fault classification for wind turbine systems using principal component analysis (PCA). Experimentations are performed in terms of two topologies of faulty datasets which are respectively actuator effectiveness loss and sinusoidal fault, occurring in a 4.8-MW wind turbine benchmark system. In order to evaluate the PCA technique, additive white Gaussian noise (AWGN) signals are introduced to the aforementioned two faulty conditions respectively. The experimental results demonstrate that PCA not only can extract the significant features but also distinguish different types of fault effectively in this wind turbine benchmark system." @default.
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- W3003306007 date "2019-07-01" @default.
- W3003306007 modified "2023-09-24" @default.
- W3003306007 title "Fault Classification in Wind Turbines Using Principal Component Analysis Technique" @default.
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- W3003306007 doi "https://doi.org/10.1109/indin41052.2019.8972303" @default.
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