과제정보
This work was supported by "The development of a fully electrified car ferry and a removable power supply system (Project No. 20200469-01, PMS4700) funded by a national R&D project of the Ministry of Oceans and Fisheries. The authors gratefully would like to express our sincere gratitude for the research fund granted.
참고문헌
- Bach-Andersen, M., Romer-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., Romer-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
- Cho, K.H., Merrienboer, B.V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, 1724-1734. https://doi.org/10.3115/v1/D14-1179
- Cho, S., Gao, Z., & Moan, T. (2018). Model-based Fault Detection, Fault Isolation and Fault-tolerant Control of a Blade Pitch System in Floating Wind Turbines. Renewable Energy, 120, 306-321. https://doi.org/10.1016/j.renene.2017.12.102
- Cho, S., Bachynski, E.E., Nejad, A.R., Gao, Z., & Moan, T. (2020). Numerical Modeling of Hydraulic Blade Pitch Actuator in a Spar-type Floating Wind Turbine Considering Fault Conditions and Their Effects on Global Dynamic Responses. Wind Energy, 23(2), 370-390. https://doi.org/10.1002/we.2438
- Cho, S., Choi, M.J., Gao, Z., & Moan, T. (2021). Fault Detection and Diagnosis of a Blade Pitch System in a Floating Wind Turbine Based on Kalman Filters and Artificial Neural Networks. Renewable Energy, 169, 1-13. https://doi.org/10.1016/j.renene.2020.12.116
- Dervilis, N., Choi, M., Taylor, S.G., Barthorpe, R.J., Park, G., Farrar, C.R., & Worden, K. (2014). On Damage Diagnosis for a Wind Turbine Blade Using Pattern Recognition. Journal of Sound and Vibration, 333(6), 1833-1850. https://doi.org/10.1016/j.jsv.2013.11.015
- Dinwoodie, I., McMillan, D., Revie, M., Lazakis, I., & Dalgic, Y. (2013). Development of a Combined Operational and Strategic Decision Support Model for Offshore Wind. Energy Procediam, 35, 157-166. https://doi.org/10.1016/j.egypro.2013.07.169
- Gayo, J.B. (2011). Reliability-focused Research on Optimizing Wind Energy System Design, Operation and Maintenance: Tools, Proof of Concepts, Guidelines & Methodologies for a New Generation (FP7-Energy-2007-1-RTD). ReliaWind.
- Hasselmann, K., Barnett, T.P., Bouws, E., Carlson, H., Cartwright, D.E., Enke, K., ... Meerburg, A., (1973). Measurements of Wind-wave Growth and Swell Decay During the Joint North Sea Wave Project (JONSWAP). Erganzungsheft zur Deutschen Hydrographischen Zeitschrift, Reihe A, 12.
- IEC (2005a). Wind Turbines - Part 1: Design Requirements (IEC 61400-1). International Electrotechnical Commission.
- IEC (2005b). Wind Turbines - Part 3: Design Requirements for Offshore Wind Turbines (IEC 61400-3). International Electrotechnical Commission.
- Isermann, R. (2006). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Germany: Springer.
- Jiang, G., Xie, P., He, H., & Yan, J. (2017). Wind Turbine Fault Detection Using a Denoising Autoencoder with Temporal Information. IEEE/ASME Transactions on Mechatronics, 23(1), 89-100. https://doi.org/10.1109/TMECH.2017.2759301
- Johannessen K, Meling T.S., Haver S. (2002). Joint Distribution for Wind and Waves in the Northern North Sea. International Journal of Offshore and Polar Engineering, 12(1), ISOPE02-12-1-001.
- Jonkman, J., Butterfield, S., Musial, W., & Scott, G. (2009). Definition of a 5-MW Reference Wind Turbine for Offshore System Development (Technical Report NREL/TP500-38060). USA: National Renewable Energy Lab.
- Jonkman, J. (2010). Definition of the Floating System for Phase IV of OC3 Technical Report NREL/TP-500-47535 USA.
- Jonkman, J., & Kilcher, L. (2012). TurbSim User's Guide Technical Report NREL USA.
- Kusiak, A., & Li, W. (2011). The Prediction and Diagnosis of Wind Turbine Faults. Renewable Energy, 36(1), 16-23. https://doi.org/10.1016/j.renene.2010.05.014
- Kusiak, A., & Verma, A. (2012). Analyzing Bearing Faults in Wind Turbines: A Data-mining Approach. Renewable Energy, 48, 110-116. https://doi.org/10.1016/j.renene.2012.04.020
- Laouti, N., Sheibat-Othman, N., & Othman, S. (2011). Support Vector Machines for Fault Detection in Wind Turbines. Proceedings of 18th IFAC World Congress, Milano, Italy, 44(1), 7067-7072. https://doi.org/10.3182/20110828-6-IT-1002.02560
- NordzeeWind. (2010). Operations Report 2009 (Technical Report OWEZ_R_000_20101112). NordzeeWind.
- Santos, P., Villa, L.F., Renones, A., Bustillo, A., & Maudes, J. (2015). An SVM-based Solution for Fault Detection in Wind Turbines. Sensors, 15(3), 5627-5648. https://doi.org/10.3390/s150305627
- SINTEF Ocean. (2018a). SIMO 4.15.0 User Guide.
- SINTEF Ocean. (2018b). RIFLEX 4.14.0 User Guide.
- Simani, S., Farsoni, S., & Castaldi, P. (2014). Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models. IEEE Transactions on Industrial Electronics, 62(6), 3775-3782. https://doi.org/10.1109/TIE.2014.2364548
- Wang, L., Zhang, Z., Long, H., Xu, J., & Liu, R. (2016). Wind Turbine Gearbox Failure Identification with Deep Neural Networks. IEEE Transactions on Industrial Informatics, 13(3), 1360-1368. https://doi.org/10.1109/TII.2016.2607179
- Wason, R. (2018). Deep Learning: Evolution and Expansion. Cognitive Systems Research, 52, 701-708. https://doi.org/10.1016/j.cogsys.2018.08.023
- Zaher, A., McArthur, S.D.J., Infield, D.G., & Patel, Y. (2009). Online Wind Turbine Fault Detection Through Automated SCADA Data Analysis. Wind Energy, 12(6), 574-593. https://doi.org/10.1002/we.319
- Zeng, J., Lu, D., Zhao, Y., Zhang, Z., Qiao, W., & Gong, X. (2013). Wind Turbine Fault Detection and Isolation Using Support Vector Machine and a Residual-based Method. 2013 American Control Conference, Washington, DC, USA, 3661-3666. https://doi.org/10.1109/ACC.2013.6580398