Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data | Zendy

Nils Schwenzfeier | Zendy; Markus Heikamp | Zendy; Ole Meyer | Zendy; Andre Hönnscheidt | Zendy; Michael W. Steffes | Zendy; Volker Gruhn | Zendy

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Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data

Author(s) -

Nils Schwenzfeier,

Markus Heikamp,

Ole Meyer,

Andre Hönnscheidt,

Michael W. Steffes,

Volker Gruhn

Publication year - 2022

Publication title -

proceedings of the aaai conference on artificial intelligence

Language(s) - Uncategorized

Resource type - Journals

eISSN - 2374-3468

pISSN - 2159-5399

DOI - 10.1609/aaai.v36i11.21533

Subject(s) - wind power , computer science , outlier , anomaly detection , reliability (semiconductor) , turbine , reliability engineering , fault (geology) , renewable energy , grid , fault detection and isolation , data set , data mining , energy (signal processing) , set (abstract data type) , real time computing , converters , power (physics) , artificial intelligence , engineering , electrical engineering , voltage , actuator , statistics , physics , geometry , mathematics , quantum mechanics , seismology , geology , programming language , mechanical engineering

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