Abstract
The conventional approaches for modeling dynamic systems are based on state-space methods using differential algebraic equations (DAEs). Such models not only require that the system dynamics can be precisely captured and expressed in mathematical equations, but also need detailed knowledge about the system parameters. Even when such DAEs are available, no closed-form solutions are available, and numerical solutions can be computationally expensive. As an example, modern power systems are typically large complex networks comprising of hundreds or even thousands of buses. The dimension of the mathematical models can easily reach the order of several thousands of state variables for dynamic simulation, trajectory sensitivity analysis, control, and so forth. Therefore, analyzing these extremely high-order DAEs poses a huge computational burden [1].
| Original language | English (US) |
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| Title of host publication | GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728127231 |
| DOIs | |
| State | Published - Nov 2019 |
| Event | 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada Duration: Nov 11 2019 → Nov 14 2019 |
Publication series
| Name | GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings |
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Conference
| Conference | 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 |
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| Country/Territory | Canada |
| City | Ottawa |
| Period | 11/11/19 → 11/14/19 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT The research was supported in part by US DoD DTRA grant HDTRA1-14-1-0040, and NSF grants CNS 1831140, 1814322 and 1901103.
Publisher Copyright:
© 2019 IEEE.