Dr HABIBU HUSSAINI Habibu.Hussaini1@nottingham.ac.uk
RESEARCH FELLOW
Dr HABIBU HUSSAINI Habibu.Hussaini1@nottingham.ac.uk
RESEARCH FELLOW
Professor TAO YANG TAO.YANG@NOTTINGHAM.AC.UK
PROFESSOR OF AEROSPACE ELECTRICALSYSTEMS
Cheng Wang
Professor SERHIY BOZHKO serhiy.bozhko@nottingham.ac.uk
PROFESSOR OF AIRCRAFT ELECTRIC POWER SYSTEMS
The more electric aircraft concept has been identified as the major trend of future aircraft. The DC distribution network where multiple electrical sources are connected to a common HVDC bus is a promising architecture for more electric aircraft application. The power sharing of these sources is achieved using droop control. However, the conventional droop control method has a limitation in achieving accurate load sharing and voltage regulation due to the influence of the cable resistance and nominal voltage reference offset. In this paper, an enhanced droop control method is proposed for more electric aircraft application. The proposed strategy compensates the droop coefficient of each subsystem according to the estimated average total cable resistance. This is implemented with the aid of a compensating link in order to mitigate the influence of cable resistance on accurate current sharing. Also, the DC bus voltage restoration is realized by adjusting the sources references according to the product of the total load current and global droop gain with the aid of a feedforward link. The method is simple and can be easily implemented without the need for an extra communication link. The effectiveness of the proposed method has been validated through simulation.
Hussaini, H., Yang, T., Wang, C., & Bozho, S. (2021, October). An Enhanced Droop Control Method for multi-source Electric Power System of More Electric Aircraft. Paper presented at MEA2021, Bordeaux, France
Presentation Conference Type | Conference Paper (unpublished) |
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Conference Name | MEA2021 |
Start Date | Oct 20, 2021 |
End Date | Oct 21, 2021 |
Deposit Date | Sep 7, 2021 |
Peer Reviewed | Not Peer Reviewed |
Public URL | https://nottingham-repository.worktribe.com/output/6187047 |
MEA 2021 Paper 25 (003)
(584 Kb)
PDF
Inverse application of artificial intelligence for the control of power converters
(2022)
Journal Article
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