Droop Coefficient Design in Droop Control of Power Converters for Improved Load Sharing: An Artificial Neural Network Approach

Hussaini, Habibu; Yang, Tao; Gao, Yuan; Wang, Cheng; Dragicevic, Tomislav; Bozhko, Serhiy

Abstract

In this paper, a new approach for the design of the droop coefficient in the droop control of power converters using the artificial neural network (ANN) is proposed. In the first instance, a detailed more electric aircraft (MEA) electrical power system (EPS) circuit model is simulated in a loop using different combinations of the converters droop coefficients within a design space. The inaccurate output DC currents sharing of the converters due to the influence of the unequal cable resistance are then obtained from each of the simulations. The data generated is then used to train the NN to be a dedicated surrogate model of the detailed MEA EPS simulation. Thus, for any user-defined desired current sharing among the converters that are within the design space, the proposed NN can provide the optimal droop coefficients. This NN approach has been verified through simulations to ensure accurate current sharing between the converters as desired. Hence, can be used in the design of the droop coefficient to enhance the performance of the conventional droop control method.

Citation

Hussaini, H., Yang, T., Gao, Y., Wang, C., Dragicevic, T., & Bozhko, S. (2021). Droop Coefficient Design in Droop Control of Power Converters for Improved Load Sharing: An Artificial Neural Network Approach. In Proceedings of 30th International Symposium on Industrial Electronics (ISIE 2021). https://doi.org/10.1109/isie45552.2021.9576482

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)
• Start Date: Jun 20, 2021
• End Date: Jun 23, 2021
• Acceptance Date: Apr 17, 2021
• Online Publication Date: Nov 13, 2021
• Publication Date: Jun 23, 2021
• Deposit Date: Jun 4, 2021
• Publicly Available Date: Jun 23, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Series Title: International Symposium on Industrial Electronics
• Series ISSN: 2163-5145
• Book Title: Proceedings of 30th International Symposium on Industrial Electronics (ISIE 2021)
• ISBN: 9781728190242
• DOI: https://doi.org/10.1109/isie45552.2021.9576482
• Keywords: Training , Resistance , Power cables , Atmospheric modeling , Artificial neural networks , Aerospace electronics , Data models, Artificial neural network , Droop coefficient , Cable resistance , More electric aircraft , Data generation
• Public URL: https://nottingham-repository.worktribe.com/output/5625403
• Publisher URL: https://ieeexplore.ieee.org/document/9576482
• Related Public URLs: https://www.isie2021.org/index.html
• Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.