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- W4382996681 abstract "Predicting a potential fault in wind turbines provides huge benefits. The data collected on the SCADA system monitoring the operational status of wind turbines contain information about wind turbine health. In this study, operational data from a wind farm in Turkey were analysed. This chapter presents condition monitoring approaches that can be used to predict wind turbine failures before they happen. First, data points from 5 days prior to the faults were predicted using the glass-box classification model. Features and feature combinations have been discovered to be of greatest importance to the success of this pre-fault prediction. Then, the condition monitoring method is proposed using LSTM autoencoder model with significant feature and feature combinations. For each model developed, a well-defined threshold value was determined by analysis of the reconstruction error. As a result of the anomaly score exceeding the threshold value, a malfunction usually occurs within 5 days." @default.
- W4382996681 created "2023-07-04" @default.
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- W4382996681 date "2023-01-01" @default.
- W4382996681 modified "2023-09-27" @default.
- W4382996681 title "Wind Turbine Condition Monitoring Using Failure Analysis" @default.
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- W4382996681 doi "https://doi.org/10.1007/978-3-031-30171-1_42" @default.
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