Training Neural-Network-Based Controller on Distributed Machine Learning Platform for Power Electronics Systems

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

17 Scopus Citations
View graph of relations

Author(s)

  • Wenguan Wang
  • Ralph Cheng
  • Alan Wai-lun Lo
  • J. Kwok
  • Jun Zhang

Detail(s)

Original languageEnglish
Title of host publication2017 IEEE Energy Conversion Congress and Exposition (ECCE)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages3083-3089
ISBN (electronic)9781509029983
Publication statusPublished - Nov 2017

Publication series

NameIEEE Energy Conversion Congress and Exposition, ECCE
PublisherIEEE

Conference

Title9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017
LocationDuke Energy Convention Center
PlaceUnited States
CityCincinnati
Period1 - 5 October 2017

Abstract

A new training scheme for neural-network-based controller for power electronics systems is proposed. It utilizes the circuit model of the power conversion stage (PCS) in the training process. The training algorithm is a distributed form of evolutionary computation, being able to run on a computer cluster equipped with multiple graphics processing units (GPUs). As a design example, a boost converter has been built and evaluated to exemplify the performance of the neural-network controller trained by the proposed scheme. The experimental results showed that the neural-network controller excels in speed of response and rejection of disturbance once trained properly.

Research Area(s)

  • Artificial intelligence, Distributed computing, Evolutionary computation, Graphics processing units, Neural network, Power electronics

Citation Format(s)

Training Neural-Network-Based Controller on Distributed Machine Learning Platform for Power Electronics Systems. / Wang, Wenguan; Chung, Henry Shu-hung; Cheng, Ralph et al.
2017 IEEE Energy Conversion Congress and Exposition (ECCE). Institute of Electrical and Electronics Engineers, Inc., 2017. p. 3083-3089 8096563 (IEEE Energy Conversion Congress and Exposition, ECCE).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review