Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Performance Limitations of Distributed Integral Control in Power Networks under Noisy Measurements

Flamme, Hendrik ; Tegling, Emma LU and Sandberg, Henrik LU (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.

(Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
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}},
}