Cleaned and Preprocessed Aventa AV-7 Wind Turbine SCADA Dataset for Anomaly Detection and Cybersecurity Research 机器收录·待核验

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This dataset is a preprocessed version of the Aventa AV-7 IET-OST wind turbine SCADA dataset. It has been cleaned using a combination of unsupervised machine learning methods (such as Isolation Forest and Autoencoders) and domain-specific rule-based filtering.

The purpose of this preprocessing is to remove noisy, unrealistic, or physically implausible records that could result from sensor malfunctions or data corruption. No artificial data has been introduced into this version. The cleaning process strictly reflects operational constraints and climate conditions consistent with the original turbine's physical context in Switzerland.

The resulting dataset (~35.47 million records) preserves only realistic wind turbine telemetry and is suitable for SCADA anomaly detection, sensor fault analysis, and operational modeling. It is intended as a trustworthy dataset for researchers needing high-fidelity SCADA telemetry without injected threats.

for considerations wind turbine local engineering system (cold/no heating) rows with Generator Temperature lower than 5 degrees: Rows with GeneratorTemperature < 5 degrees: 684521Percentage of total rows: 1.93%

Sample of rows with GeneratorTemperature < 5 degrees:                       Datetime  GeneratorTemperature  GeneratorSpeed  WindSpeed  RotorSpeed  PowerOutput107888  2022-01-02 05:03:57.209                   4.9             0.0        0.0         0.0          0.0107889  2022-01-02 05:03:58.209                   4.9             0.0        0.0         0.0          0.0107890  2022-01-02 05:03:59.207                   4.9             0.0        0.0         0.0          0.0107891  2022-01-02 05:04:00.206                   4.9             0.0        0.0         0.0          0.0107892  2022-01-02 05:04:01.203                   4.9             0.0        0.0         0.0          0.0

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https://zenodo.org/records/15319922

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CC-BY-4.0 (置信:verified_official)

发布年份

2025

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Zenodo

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