Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower
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Published
Sep 4, 2023
Yu Jia
Xiaomo Jiang
Abstract
Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.
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Keywords
Digital Twin, Status warning, Delegation model, Tower mechanism simulation
References
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Xu, Y., Sun, W. L., & Zhou, J. P. (2008). Static and dynamic analysis of wind turbine tower structure. Advanced Materials Research (Vol. 33, pp. 1169-1174). Trans Tech Publications Ltd. doi:10.4028/www.scientific.net/amr.33-37.1169
Kudela, J., & Matousek, R. (2022). Recent advances and applications of surrogate models for finite element method computations: a review. Soft Computing, 26(24), 13709-13733. doi:10.1007/s00500-022-07362 8
Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Publications of the American Statistical Association, 58(302), 415-434. doi:10.1080/01621459.1963.10500855
Breiman, L., & Ihaka, R. (1984). Nonlinear discriminant analysis via scaling and ACE. Davis One Shields Avenue Davis, CA, USA: Department of Statistics, University of California.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270. doi:10.1162/neco_a_01199
Elsaraiti, M., & Merabet, A. (2021). A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed. Energies, 14(20), 6782.doi:10.3390/en14206782
Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., & Zheng, M. (2019). Wind power short-term prediction based on LSTM and discrete wavelet transform. Applied Sciences, 9(6), 1108.doi:10.3390/app9061108
Choe, D. E., Kim, H. C., & Kim, M. H. (2021). Sequencebased modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades. Renewable Energy, 174, 218-235.doi:10.1016/j.renene.2021.04.025
Lei, J., Liu, C., & Jiang, D. (2019). Fault diagnosis of wind turbine based on Long Short-term memory networks. Renewable energy, 133, 422 432.doi:10.1016/j.renene.2018.10.031
Xu, Y., Sun, W. L., & Zhou, J. P. (2008). Static and dynamic analysis of wind turbine tower structure. Advanced Materials Research (Vol. 33, pp. 1169-1174). Trans Tech Publications Ltd. doi:10.4028/www.scientific.net/amr.33-37.1169
Kudela, J., & Matousek, R. (2022). Recent advances and applications of surrogate models for finite element method computations: a review. Soft Computing, 26(24), 13709-13733. doi:10.1007/s00500-022-07362 8
Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Publications of the American Statistical Association, 58(302), 415-434. doi:10.1080/01621459.1963.10500855
Breiman, L., & Ihaka, R. (1984). Nonlinear discriminant analysis via scaling and ACE. Davis One Shields Avenue Davis, CA, USA: Department of Statistics, University of California.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270. doi:10.1162/neco_a_01199
Elsaraiti, M., & Merabet, A. (2021). A comparative analysis of the arima and lstm predictive models and their effectiveness for predicting wind speed. Energies, 14(20), 6782.doi:10.3390/en14206782
Liu, Y., Guan, L., Hou, C., Han, H., Liu, Z., Sun, Y., & Zheng, M. (2019). Wind power short-term prediction based on LSTM and discrete wavelet transform. Applied Sciences, 9(6), 1108.doi:10.3390/app9061108
Choe, D. E., Kim, H. C., & Kim, M. H. (2021). Sequencebased modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades. Renewable Energy, 174, 218-235.doi:10.1016/j.renene.2021.04.025
Lei, J., Liu, C., & Jiang, D. (2019). Fault diagnosis of wind turbine based on Long Short-term memory networks. Renewable energy, 133, 422 432.doi:10.1016/j.renene.2018.10.031
Section
Regular Session Papers
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