Abstract
In this paper, a new near optimal supplementary damping controller is proposed for grid connected dc microgrids with small power capacities using the Q-learning algorithm. A novel system discretization approach is presented to formulate the power system stability problem into a Q-learning solvable format. A numerical example is provided for illustration of the performance of the proposed supplementary damping controller.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 |
| Publisher | IEEE Computer Society |
| Volume | 2016-November |
| ISBN (Electronic) | 9781509041688 |
| DOIs | |
| State | Published - Nov 10 2016 |
| Event | 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: Jul 17 2016 → Jul 21 2016 |
Other
| Other | 2016 IEEE Power and Energy Society General Meeting, PESGM 2016 |
|---|---|
| Country | United States |
| City | Boston |
| Period | 7/17/16 → 7/21/16 |
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Keywords
- Damping
- Micro-grids
- Q-learning
- Small signal stability
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
Cite this
Supplementary damping controller of grid connected dc micro-grids based on Q-learning. / Hong, Tianqi; Bian, Tao; De Leon, Francisco.
2016 IEEE Power and Energy Society General Meeting, PESGM 2016. Vol. 2016-November IEEE Computer Society, 2016. 7741189.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Supplementary damping controller of grid connected dc micro-grids based on Q-learning
AU - Hong, Tianqi
AU - Bian, Tao
AU - De Leon, Francisco
PY - 2016/11/10
Y1 - 2016/11/10
N2 - In this paper, a new near optimal supplementary damping controller is proposed for grid connected dc microgrids with small power capacities using the Q-learning algorithm. A novel system discretization approach is presented to formulate the power system stability problem into a Q-learning solvable format. A numerical example is provided for illustration of the performance of the proposed supplementary damping controller.
AB - In this paper, a new near optimal supplementary damping controller is proposed for grid connected dc microgrids with small power capacities using the Q-learning algorithm. A novel system discretization approach is presented to formulate the power system stability problem into a Q-learning solvable format. A numerical example is provided for illustration of the performance of the proposed supplementary damping controller.
KW - Damping
KW - Micro-grids
KW - Q-learning
KW - Small signal stability
UR - http://www.scopus.com/inward/record.url?scp=85002249089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85002249089&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2016.7741189
DO - 10.1109/PESGM.2016.7741189
M3 - Conference contribution
AN - SCOPUS:85002249089
VL - 2016-November
BT - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016
PB - IEEE Computer Society
ER -