Skip to main content

Research Repository

Advanced Search

Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft

Hussaini, Habibu; Yang, Tao; Gao, Yuan; Wang, Cheng; Urrutia, Matías; Bozhko, Serhiy

Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft Thumbnail


Authors

TAO YANG TAO.YANG@NOTTINGHAM.AC.UK
Associate Professor

Yuan Gao

Cheng Wang

Matías Urrutia



Abstract

The design of the droop coefficient is one of the challenges for the droop control of converters, as it plays a key role in enhancing the performance of the droop control method. This article proposes an artificial neural network (ANN) based technique for the design of optimal droop control of parallel-connected converters in a fast and accurate manner without imposing an additional computational burden on the system. The developed ANN-based design strategy of droop coefficients is used for load sharing and dc bus voltage regulation for the more electric aircraft (MEA) application. In the design process, the optimal droop coefficient setting is obtained by evaluating a user-defined fitness function with the aid of a trained ANN-based surrogate model. It is observed that the system performance metrics predicted by the surrogate model matched very well with that obtained from the simulation model. The experimental results show that the selected optimal droop coefficient setting can enhance the performance of the traditional droop control method in both steady and transient conditions.

Citation

Hussaini, H., Yang, T., Gao, Y., Wang, C., Urrutia, M., & Bozhko, S. (2024). Optimal Droop Control Design Using Artificial Intelligent Techniques for Electric Power Systems of More-Electric Aircraft. IEEE Transactions on Transportation Electrification, 10(1), 2192-2206. https://doi.org/10.1109/tte.2023.3271763

Journal Article Type Article
Acceptance Date Apr 10, 2023
Online Publication Date May 3, 2023
Publication Date 2024-03
Deposit Date Apr 29, 2024
Publicly Available Date Apr 29, 2024
Journal IEEE Transactions on Transportation Electrification
Electronic ISSN 2332-7782
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 10
Issue 1
Pages 2192-2206
DOI https://doi.org/10.1109/tte.2023.3271763
Public URL https://nottingham-repository.worktribe.com/output/20287876
Publisher URL https://ieeexplore.ieee.org/document/10115011

Files





You might also like



Downloadable Citations