Surrogate-Based Optimization of Firing Angles for Switched Reluctance Motor

Bahareh Anvari, Mine Kaya, Steven Englebretson, Shima Hajimirza, Hamid A. Toliyat

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this work, optimal firing angles of Switched Reluctance Motors (SRMs) are explored by surrogatebased optimization in order to minimize the torque ripple. Surrogate-based optimization is facilitated via Neural Networks (NNs) which are regression tools capable of learning complex multi-variate functions. Flux and torque calculations of a nonlinear 16/20 SRM are evaluated with a NN, and consequently the computation time is expedited by replacing the look-up tables of flux and torque with the surrogate NN model. An optimization algorithm is proposed to discover optimal firing angle objects to minimize the 16/20 SRM torque ripple for a certain electrical load requirement. The resulting optimal firing angles are also represented by simple NN models to expedite online control. Comprehensive simulation and experimental results are provided to validate the theoretical findings.

Original languageEnglish
Title of host publication2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
Pages359-365
Number of pages7
DOIs
StatePublished - 28 Aug 2018
Event2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018 - Long Beach, United States
Duration: 13 Jun 201815 Jun 2018

Publication series

Name2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018

Conference

Conference2018 IEEE Transportation and Electrification Conference and Expo, ITEC 2018
Country/TerritoryUnited States
CityLong Beach
Period13/06/1815/06/18

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