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- W3100483711 abstract "Cup anemometers are widely used instruments for wind turbines to measure wind speed in wind farm. Aimed to reduce the adverse impact on wind energy resource estimation, this paper proposes a data-driven fault diagnosis approach for assessing the anemometer health status. Auto-associative netural network (AANN) is developed to reconstruct the anemometer measurement data after data pre-processing, and residual analysis is performed between the anemometer measurement data and the AANN reconstruction data. In addition, the quantitative indicators that can reflect the health status of the anemometer gained from residuals are obtained through the K-Means clustering algorithm, based on which the faulty anemometers in the wind farm can be identified. The approach can provide guidance for the production and operation of the wind farm." @default.
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- W3100483711 date "2020-10-18" @default.
- W3100483711 modified "2023-10-17" @default.
- W3100483711 title "A Data-Driven Fault Diagnosis Approach for Anemometers in Wind Farm" @default.
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- W3100483711 doi "https://doi.org/10.1109/iecon43393.2020.9254833" @default.
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