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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

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

February 1, 2023

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Authors

Nils Schwenzfeier

University of Duisburg-Essen


Markus Heikamp

University of Duisburg-Essen


Ole Meyer

University of Duisburg-Essen


Andre Hönnscheidt

University of Duisburg-Essen


Michael Steffes

ConverterTec Deutschland GmbH


Volker Gruhn

University of Duisburg-Essen


DOI:

10.1609/aaai.v36i11.21533


Abstract:

Wind energy is an important source of renewable and sustainable energy and therefore an elementary component of any future energy supply. However, the operation of large wind farms places high demands on reliability and is often impacted by high maintenance and repair costs in the event of a failure. A frequency converter is one of the most important components of each wind turbine, which ensures that the frequency of the generated energy synchronises with the grid frequency and thus enables the flow of energy into the power grid. The detection of anomalies in these devices is complex due to the high frequency and multidimensionality of different sensor information from the energy control units and requires fault patterns to be discovered and detected in large time series. In this paper, we show how state-of-the-art self-supervised-learning techniques, namely LSTM autoencoders, can be successfully applied to real-world data. We describe the extensions we have made to deal with the often very noisy sensors and describe the construction of the training data set. The trained system was first tested and evaluated on synthetic data and subsequently on a large real-world data set. In both cases, it was shown that outliers can be reliably identified using our presented approach.

Topics: AAAI

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HOW TO CITE:

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data Proceedings of the AAAI Conference on Artificial Intelligence (2022) 12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data AAAI 2022, 12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn (2022). Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data. Proceedings of the AAAI Conference on Artificial Intelligence, 12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. 2022. Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data. "Proceedings of the AAAI Conference on Artificial Intelligence". 12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. (2022) "Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data", Proceedings of the AAAI Conference on Artificial Intelligence, p.12601-12607

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn, "Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data", AAAI, p.12601-12607, 2022.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. "Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. "Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 12601-12607.

Nils Schwenzfeier||Markus Heikamp||Ole Meyer||Andre Hönnscheidt||Michael Steffes||Volker Gruhn. Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data. AAAI[Internet]. 2022[cited 2023]; 12601-12607.


ISSN: 2374-3468


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