Fast-slow dynamics of a hydropower generation system with multi-time scales

© 2018 Elsevier Ltd This paper reports on fast-slow dynamical analysis of a typical complex engineering system coupling with hydraulic-mechanical-electric power. Here, we find a high-dimensional hydropower generation system that fast-slow effect manifesting as spiking states and quiescent states exi...

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Main Authors: Li, H., Chen, D., Gao, X., Wang, Xiangyu, Han, Q., Wu, Changzhi
Format: Journal Article
Published: Academic Press 2018
Online Access:http://hdl.handle.net/20.500.11937/67038
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author Li, H.
Chen, D.
Gao, X.
Wang, Xiangyu
Han, Q.
Wu, Changzhi
author_facet Li, H.
Chen, D.
Gao, X.
Wang, Xiangyu
Han, Q.
Wu, Changzhi
author_sort Li, H.
building Curtin Institutional Repository
collection Online Access
description © 2018 Elsevier Ltd This paper reports on fast-slow dynamical analysis of a typical complex engineering system coupling with hydraulic-mechanical-electric power. Here, we find a high-dimensional hydropower generation system that fast-slow effect manifesting as spiking states and quiescent states exists by means of multi-time-scale structure mining. In our experimental analysis, we extract critical fast-slow variant parameters used to further study the behaviors of the presented system. Our results reveal that the change of fast-slow variant parameters has remarkable impact on the fluctuation interval of spiking states and quiescent states, which provides guidance for system parameter setting; meanwhile, we experimentally demonstrate that multi-time-scale physical phenomena reflect the stability and operational feature of the complex hydropower generation system. This work, combined with multi-scale dynamic analysis and a snapshot of macro-significance of engineering, enables us to develop a novel framework for investigating instability of complex engineering systems.
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T10:32:00Z
publishDate 2018
publisher Academic Press
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spelling curtin-20.500.11937-670382018-05-18T08:04:22Z Fast-slow dynamics of a hydropower generation system with multi-time scales Li, H. Chen, D. Gao, X. Wang, Xiangyu Han, Q. Wu, Changzhi © 2018 Elsevier Ltd This paper reports on fast-slow dynamical analysis of a typical complex engineering system coupling with hydraulic-mechanical-electric power. Here, we find a high-dimensional hydropower generation system that fast-slow effect manifesting as spiking states and quiescent states exists by means of multi-time-scale structure mining. In our experimental analysis, we extract critical fast-slow variant parameters used to further study the behaviors of the presented system. Our results reveal that the change of fast-slow variant parameters has remarkable impact on the fluctuation interval of spiking states and quiescent states, which provides guidance for system parameter setting; meanwhile, we experimentally demonstrate that multi-time-scale physical phenomena reflect the stability and operational feature of the complex hydropower generation system. This work, combined with multi-scale dynamic analysis and a snapshot of macro-significance of engineering, enables us to develop a novel framework for investigating instability of complex engineering systems. 2018 Journal Article http://hdl.handle.net/20.500.11937/67038 10.1016/j.ymssp.2018.03.028 Academic Press restricted
spellingShingle Li, H.
Chen, D.
Gao, X.
Wang, Xiangyu
Han, Q.
Wu, Changzhi
Fast-slow dynamics of a hydropower generation system with multi-time scales
title Fast-slow dynamics of a hydropower generation system with multi-time scales
title_full Fast-slow dynamics of a hydropower generation system with multi-time scales
title_fullStr Fast-slow dynamics of a hydropower generation system with multi-time scales
title_full_unstemmed Fast-slow dynamics of a hydropower generation system with multi-time scales
title_short Fast-slow dynamics of a hydropower generation system with multi-time scales
title_sort fast-slow dynamics of a hydropower generation system with multi-time scales
url http://hdl.handle.net/20.500.11937/67038