Parallel processing and genetic algorithm‐based efficiency optimization for multiconverter power system. (June 2017)

• Record Type: Journal Article
• Authors: Xie, Mingfeng; Qian, Qinsong; Yu, Juzheng; Sun, Weifeng; Lu, Shengli
• Is Part Of: IEEJ transactions on electrical and electronic engineering. Volume 12(2017)Supplement 1
• Page Start: S179
• Page End: S180
• Publication Date: 2017-06
• Subjects: parallel processing -- genetic algorithm -- multiconverter power system -- efficient optimization; Electrical engineering -- Periodicals; Electronics -- Periodicals; 621.3
• DOI: 10.1002/tee.22431
• Languages: English
• ISSNs: 1931-4973

Abstract

Abstract : An efficient optimization approach based on parallel processing and genetic algorithm for a multiconverter power system is presented in this paper. The system consists of several clients and one server. The genetic algorithm helps the system to reach maximum efficiency over the entire load range. Meanwhile, WiFi is employed to achieve high‐speed data transfer. Also, a benefit from the parallel processing and structure is that the optimization time has been exponentially reduced. The experimental results show that the prototype can achieve optimal efficiency over the entire load range, and the optimizing process is accelerated five times in average because of the parallel processing © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.