A Continual Learning-based Framework for Developing A Single Wind Turbine Cybertwin Adaptively Serving Multiple Modeling Tasks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)4912-4921
Number of pages10
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume18
Issue number7
Online published25 Nov 2021
Publication statusOnline published - 25 Nov 2021

Abstract

This paper proposes a generalized neural continual learning-based cybertwin (GNC) modeling framework to realize developing one wind turbine (WT) cybertwin serving multiple modeling tasks in the wind farm operations and maintenance (O&M). A generalized WT cybertwin modeling problem, which considers modeling one cybertwin for multiple tasks without additional computational burden, is studied for the first time. Fully connected deep neural networks (DNNs) are adopted as the backbone for developing the GNC model. The online elastic weight consolidation (OEWC) method is incorporated to mitigate the catastrophic forgetting phenomenon among different modeling tasks. Computational experiments are conducted to validate the effectiveness of the proposed GNC framework based on the supervisory control and data acquisition (SCADA) data. Modeling tasks in three important problems of the wind farm O&M, the WT gearbox failure detection, WT blade breakage detection, and wind power prediction, are considered in the experiment. Compared with other benchmarking models, such as the multiple neural cybertwins, neural cybertwin , and regularized neural cybertwin, the proposed GNC can achieve high accuracies on both new tasks and existing tasks, which further verifies the WT cybertwin generalization via the proposed GNC.

Research Area(s)

  • Blades, Computational modeling, Condition monitoring, Data models, Task analysis, Wind farms, Wind turbines, Cybertwin modeling, wind farm, neural networks (NNs), continual learning, data-driven method

Citation Format(s)

A Continual Learning-based Framework for Developing A Single Wind Turbine Cybertwin Adaptively Serving Multiple Modeling Tasks. / Yang, Luoxiao; Wang, Long; Zheng, Zhong; Zhang, Zijun.

In: IEEE Transactions on Industrial Informatics, Vol. 18, No. 7, 07.2022, p. 4912-4921.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review