Eur. Phys. J. AP
Volume 11, Number 1, July 2000
|Page(s)||21 - 27|
|Published online||15 July 2000|
Neural control and transient analysis of the LCL-type resonant converter
University Mohammed 1er, École Supérieure de Technologie, Oujda Hay EL QODS, Complexe Universitaire,
BP 473, 60000 Oujda, Morocco
2 Computer & Electrical Engineering department, College of Engineering Michigan State University, USA
Corresponding author: email@example.com
Revised: 25 January 2000
Accepted: 7 April 2000
Published online: 15 July 2000
This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.
PACS: 84.30.Jc – Power electronics; power supply circuits / 84.35.i – Neural networks
© EDP Sciences, 2000
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.