Performance Limitations of Distributed Integral Control in Power Networks under Noisy Measurements
(2018) 2018 Annual American Control Conference, ACC 2018 In Proceedings of the American Control Conference 2018-June. p.5380-5386- Abstract
Distributed approaches to secondary frequency control have become a way to address the need for more flexible control schemes in power networks with increasingly distributed generation. The distributed averaging proportional-integral (DAPI) controller presents one such approach. In this paper, we analyze the transient performance of this controller, and specifically address the question of its performance under noisy frequency measurements. Performance is analyzed in terms of an H2 norm metric that quantifies power losses incurred in the synchronization transient. While previous studies have shown that the DAPI controller performs well, in particular in sparse networks and compared to a centralized averaging PI (CAPI)... (More)
Distributed approaches to secondary frequency control have become a way to address the need for more flexible control schemes in power networks with increasingly distributed generation. The distributed averaging proportional-integral (DAPI) controller presents one such approach. In this paper, we analyze the transient performance of this controller, and specifically address the question of its performance under noisy frequency measurements. Performance is analyzed in terms of an H2 norm metric that quantifies power losses incurred in the synchronization transient. While previous studies have shown that the DAPI controller performs well, in particular in sparse networks and compared to a centralized averaging PI (CAPI) controller, our results prove that additive measurement noise may have a significant negative impact on its performance and scalability. This impact is shown to decrease with an increased inter-nodal alignment of the controllers' integral states, either through increased gains or increased connectivity. For very large and sparse networks, however, the requirement for inter-nodal alignment is so large that a CAPI approach may be preferable. Overall, our results show that distributed secondary frequency control through DAPI is possible and may perform well also under noisy measurements, but requires careful tuning.
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- author
- Flamme, Hendrik ; Tegling, Emma LU and Sandberg, Henrik LU
- publishing date
- 2018-08-09
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2018 Annual American Control Conference, ACC 2018
- series title
- Proceedings of the American Control Conference
- volume
- 2018-June
- article number
- 8431122
- pages
- 7 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2018 Annual American Control Conference, ACC 2018
- conference location
- Milwauke, United States
- conference dates
- 2018-06-27 - 2018-06-29
- external identifiers
-
- scopus:85052572852
- ISSN
- 0743-1619
- ISBN
- 9781538654286
- DOI
- 10.23919/ACC.2018.8431122
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2018 AACC.
- id
- afeb82bd-276e-4546-971a-28cb169c61c9
- date added to LUP
- 2021-11-24 09:53:23
- date last changed
- 2022-04-27 06:05:43
@inproceedings{afeb82bd-276e-4546-971a-28cb169c61c9, abstract = {{<p>Distributed approaches to secondary frequency control have become a way to address the need for more flexible control schemes in power networks with increasingly distributed generation. The distributed averaging proportional-integral (DAPI) controller presents one such approach. In this paper, we analyze the transient performance of this controller, and specifically address the question of its performance under noisy frequency measurements. Performance is analyzed in terms of an H<sub>2</sub> norm metric that quantifies power losses incurred in the synchronization transient. While previous studies have shown that the DAPI controller performs well, in particular in sparse networks and compared to a centralized averaging PI (CAPI) controller, our results prove that additive measurement noise may have a significant negative impact on its performance and scalability. This impact is shown to decrease with an increased inter-nodal alignment of the controllers' integral states, either through increased gains or increased connectivity. For very large and sparse networks, however, the requirement for inter-nodal alignment is so large that a CAPI approach may be preferable. Overall, our results show that distributed secondary frequency control through DAPI is possible and may perform well also under noisy measurements, but requires careful tuning.</p>}}, author = {{Flamme, Hendrik and Tegling, Emma and Sandberg, Henrik}}, booktitle = {{2018 Annual American Control Conference, ACC 2018}}, isbn = {{9781538654286}}, issn = {{0743-1619}}, language = {{eng}}, month = {{08}}, pages = {{5380--5386}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{Proceedings of the American Control Conference}}, title = {{Performance Limitations of Distributed Integral Control in Power Networks under Noisy Measurements}}, url = {{http://dx.doi.org/10.23919/ACC.2018.8431122}}, doi = {{10.23919/ACC.2018.8431122}}, volume = {{2018-June}}, year = {{2018}}, }