A stochastic event-triggered robust cubature Kalman filtering approach to power system dynamic state estimation with non-Gaussian measurement noises
Download
Download
Download
Full text for this resource is not available from the Research Repository.
Export
Li, Zhen 
ORCID: 0000-0003-1166-9482, Li, Sen, Liu, Bin ORCID: 0000-0003-3031-9592, Yu, SS ORCID: 0000-0002-2983-7344 and Shi, Peng ORCID: 0000-0001-8218-586X
(2023)
A stochastic event-triggered robust cubature Kalman filtering approach to power system dynamic state estimation with non-Gaussian measurement noises.
IEEE Transactions on Control Systems Technology, 31 (2).
pp. 889-896.
ISSN 1063-6536
Dimensions Badge
Altmetric Badge
| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/46679 |
| DOI | 10.1109/TCST.2022.3184467 |
| Official URL | https://ieeexplore.ieee.org/document/9810986 |
| Subjects | Current > FOR (2020) Classification > 4602 Artificial intelligence Current > Division/Research > Institute for Sustainable Industries and Liveable Cities |
| Keywords | renewable energy, greenhouse gas, fossil fuel, power systems, modern power grids |
| Download/View statistics | View download statistics for this item |
CORE (COnnecting REpositories)