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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.8
- 수록면
- 287 - 295 (9page)
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초록· 키워드
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This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.
목차
ABSTRACT
1. Introduction
2. Modeling and Control Method of a Wind Turbine
3. Fault Classification Method
4. Comparative Test
5. Conclusion
References
참고문헌 (33)
참고문헌 신청
Bach-Andersen, M., Rømer-Odgaard, B., & Winther, O., (2015). Scalable Systems for Early Fault Detection in Wind Turbines: A Data Driven Approach. Proceedings of the Annual Conference of the European Wind Energy Association, Paris, France.
Bach-Andersen, M., Rømer-Odgaard, B., & Winther, O. (2018) Deep Learning for Automated Drivetrain Fault Detection. Wind Energy, 21(1), 29‒41. https://doi.org/10.1002/we.2142
Badihi, H., Zhang, Y., & Hong, H. (2014). Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults. IEEE Transactions on Control Systems Technology, 23(4), 1351‒1372. https://doi.org/10.1109/TCST.2014.2364956
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schroder, J. (2006). Diagnosis and Fault-Tolerant Control (2nd ed.). Germany: Springer.
Carroll, J., McDonald, A., & McMillan, D. (2016). Failure Rate, Repair Time and Unscheduled O&M Cost Analysis of Offshore Wind Turbines. Wind Energy, 19, 1107‒1119. https://doi.org/10.1002/we.1887
Bach-Andersen, M., Rømer-Odgaard, B., & Winther, O. (2018) Deep Learning for Automated Drivetrain Fault Detection. Wind Energy, 21(1), 29‒41. https://doi.org/10.1002/we.2142
Badihi, H., Zhang, Y., & Hong, H. (2014). Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults. IEEE Transactions on Control Systems Technology, 23(4), 1351‒1372. https://doi.org/10.1109/TCST.2014.2364956
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schroder, J. (2006). Diagnosis and Fault-Tolerant Control (2nd ed.). Germany: Springer.
Carroll, J., McDonald, A., & McMillan, D. (2016). Failure Rate, Repair Time and Unscheduled O&M Cost Analysis of Offshore Wind Turbines. Wind Energy, 19, 1107‒1119. https://doi.org/10.1002/we.1887
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