Application of knowledge-embedded graph neural network for multivariate state prediction of wind turbines

Author:

刘 颖,李 阳光,瞿 树晖,董 纪昌,王 竞凡

Publisher

Science China Press., Co. Ltd.

Subject

Engineering (miscellaneous),General Computer Science

Reference21 articles.

1. Project Comprehensive Report Writing Group. Comprehensive report on China's long-term low-carbon development strategy and transformation path. China Popul Resour Environ, 2020, 30: 1--25.

2. Wang J, Liang Y, Zheng Y. An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples. Renew Energy, 2020, 145: 642-650.

3. Focken U, Lange M, Waldl H P. Previento — a wind power prediction system with an innovative upscaling algorithm. In: Proceedings of European Wind Energy Association Conference, 2001.

4. Giebel G, Badger J, Louka P, et al. Short-term forecasting using advanced physical modelling — the results of the Anemos project. In: Proceedings of European Wind Energy Conference and Exhibition, 2006.

5. Erdem E, Shi J. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy, 2011, 88: 1405-1414.

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