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Fast and Comprehensive Online Parameter Identification of Switched Reluctance Machines
- Source :
- IEEE Access, Vol 9, Pp 46985-46996 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- The switched reluctance machine has been an attractive candidate for many applications owing to its simple design and low construction costs, without the use of permanent magnets. However, the double saliency of its stator and rotor poles results in noise-causing torque ripples. And although advanced torque ripple minimization control techniques exist, they rely on modeling the machine, which in turn requires specialized offline experimental setups or online (during operation) parameter identification techniques. To date, existing online techniques are iterative without proof of convergence, do not provide all model parameters, and/or rely on a priori information that can change after the machine is commissioned. In this work, an online parameter identification method is developed with a new empirical model of its flux linkage and electromagnetic torque, to provide a complete nonlinear model of the machine. With two seconds of data collected online, all electrical and mechanical parameters are identified using a non-iterative algorithm, and so it does not pose a risk of divergence. Therefore, parameter identification can be reliably and frequently carried out at different operating conditions as the machine ages for diagnostics. Also, the resulting model is designed to be used by advanced torque ripple minimization control techniques. The implementation procedure is detailed along with simulation results to demonstrate its efficacy.
- Subjects :
- 0209 industrial biotechnology
General Computer Science
Computer science
Stator
02 engineering and technology
law.invention
Data modeling
020901 industrial engineering & automation
Switched reluctance machine
law
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
Torque
noniterative techniques
General Materials Science
Rotor (electric)
020208 electrical & electronic engineering
General Engineering
Control engineering
Flux linkage
Switched reluctance motor
online parameter identification
Identification (information)
nonlinear model
lcsh:Electrical engineering. Electronics. Nuclear engineering
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....e29850484ce51a98ef9f4bc3c7ec1754