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• W4312756095 abstract "Wind power is clean and renewable energy, which occupies an important position in the world's energy. This paper proposes a multi-regional fault detection method for wind turbines, which makes full use of multiple sensor data. Specifically, a voting-based Artificial Neural Network (ANN) is constructed to achieve 96.5% detection accuracy, which is a light-weighted model with high operating efficiency. The robustness of this model is confirmed by various numerical experiments as well. Meanwhile, the detailed experiments of several other benchmark methods illustrate that our method has far higher accuracy. Furthermore, we complete the improvement of the model referring to grid search and the accuracy of the model is enhanced to 97.5%." @default.
• W4312756095 created "2023-01-05" @default.
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• W4312756095 date "2022-08-21" @default.
• W4312756095 modified "2023-09-23" @default.
• W4312756095 title "Deployable Lightweight ANN-based Approach for Wind Turbine Fault Detection" @default.
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• W4312756095 doi "https://doi.org/10.1109/icrms55680.2022.9944585" @default.
• W4312756095 hasPublicationYear "2022" @default.
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