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Training neural-network-based controller on distributed machine learning platform for power electronics systems
- Authors
- Wang, Wenguan; Chung, Henry Shu-Hung; Cheng, Ralph; Leung, C.S.; Zhan, Xiaoqing; Wai-Lun Lo, Alan; Kwok, J.; Xue, Chun Jason; Zhang, Jun
- Issue Date
- Nov-2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Artificial intelligence; Distributed computing; Evolutionary computation; Graphics processing units; Neural network; Power electronics
- Citation
- 2017 IEEE Energy Conversion Congress and Exposition (ECCE), v.2017-January, pp 3083 - 3089
- Pages
- 7
- Indexed
- SCI
SCOPUS
- Journal Title
- 2017 IEEE Energy Conversion Congress and Exposition (ECCE)
- Volume
- 2017-January
- Start Page
- 3083
- End Page
- 3089
- ISSN
- 2329-3721
- 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. © 2017 IEEE.
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Related Researcher
- ZHANG, Jun
- ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)