Conference paper
Droop Coefficient Design in Droop Control of Power Converters for Improved Load Sharing: An Artificial Neural Network Approach
University of Nottingham1
Department of Electrical Engineering, Technical University of Denmark2
Electronics, Department of Electrical Engineering, Technical University of Denmark3
Center for Electric Power and Energy, Centers, Technical University of Denmark4
Smart Electric Components, Center for Electric Power and Energy, Centers, Technical University of Denmark5
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2021 |
Pages: | 1-6 |
Proceedings: | 30<sup>th</sup> IEEE International Symposium on Industrial Electronics |
Series: | Ieee International Symposium on Industrial Electronics |
ISBN: | 1728190223 , 1728190231 , 172819024X , 172819024x , 9781728190228 , 9781728190235 and 9781728190242 |
ISSN: | 21635145 and 21635137 |
Types: | Conference paper |
DOI: | 10.1109/ISIE45552.2021.9576482 |
ORCIDs: | Dragicevic, Tomislav |
Artificial neural network Cable resistance Data generation Droop coefficient More electric aircraft
Aerospace electronics Artificial neural networks Atmospheric modeling DC currents Data models MEA EPS simulation Power cables Resistance Training artificial neural network approach droop coefficient design droop control method more electric aircraft electrical power system circuit model neural nets power cables power converters power convertors power generation control surrogate model unequal cable resistance