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

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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of 30th IEEE International Symposium on Industrial Electronics
Number of pages6
PublisherIEEE
Publication date2021
ISBN (Print)978-1-7281-9024-2
DOIs
Publication statusPublished - 2021
Event30th IEEE International Symposium on Industrial Electronics - Virtual event, Kyoto, Japan
Duration: 20 Jun 202123 Jun 2021

Conference

Conference30th IEEE International Symposium on Industrial Electronics
LocationVirtual event
Country/TerritoryJapan
CityKyoto
Period20/06/202123/06/2021
SeriesIeee International Symposium on Industrial Electronics
ISSN2163-5145

Keywords

  • Artificial neural network
  • Cable resistance
  • Data generation
  • Droop coefficient
  • More electric aircraft

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