Use of optical fibres for multi-parameter monitoringin electrical AC machines

Hind, David Martin; Gerada, C.; Galea, Michael; Borg Bartalo, James; Fabian, Matthias; Sun, Tong; Grattan, Kenneth T.V.

Abstract

This paper describes a new approach to multi-parameter monitoring for electrical AC machines. It is demonstrated that speed, torque and temperature can be measured using optical fibres incorporating sensors in the form of fibre Bragg gratings (FBGs) distributed around the machine. One fibre can incorporate several FBGs and hence provide several measurements. Experimental results showing speed, torque, direction of rotation, stator housing vibration and temperature measured using the FBG method are presented and validated against measurements obtained from conventional sensors. The results show that the optical fibre based approach allows multiple parameters to be monitored accurately and simultaneously with only a fraction of the usual monitoring equipment required. Another advantage of the proposed method is the EMI immunity naturally provided by optical solutions. The presented measurement technique can also offer a new alternative approach to sensorless control.

Citation

Hind, D. M., Gerada, C., Galea, M., Borg Bartalo, J., Fabian, M., Sun, T., & Grattan, K. T. (2017, August). Use of optical fibres for multi-parameter monitoringin electrical AC machines. Presented at IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED 2017)

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED 2017)
• Start Date: Aug 30, 2017
• End Date: Sep 1, 2017
• Acceptance Date: Apr 15, 2017
• Online Publication Date: Oct 9, 2017
• Publication Date: Aug 30, 2017
• Deposit Date: Nov 17, 2017
• Publicly Available Date: Nov 17, 2017
• Peer Reviewed: Peer Reviewed
• ISBN: 9781509004096
• Keywords: Optical fibre; Fibre Bragg grating (FBG); Multi parameter monitoring; Health monitoring; Sensorless
• Public URL: https://nottingham-repository.worktribe.com/output/879492
• Publisher URL: http://ieeexplore.ieee.org/document/8062357/
• Related Public URLs: https://www.egr.msu.edu/sdemped2017/home
• Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
• Contract Date: Nov 17, 2017