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Sensorless Loss Model Control of the Six-Phase Induction Motor in All Speed Range by Extended Kalman Filter
- Source :
- IEEE Access, Vol 8, Pp 118741-118750 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- This paper proposes a simple extended Kalman filter (EKF) loss model controller (LMC) for efficiency improvement of a six-phase induction machine in all speed ranges. The proposed method is fast and can be operated online. If the machine parameters are changed during the operation, the EKF algorithm is activated to find the parameters to ensure optimal efficiency operation. Not only is the motor speed measurement difficult at low speeds but it is also difficult to calculate the machine efficiency at the same speeds. Thus, the EKF model can estimate speed, load, and motor efficiency at low speed ranges so that optimization can be done in all loads and speed ranges. Unlike the conventional LMC method, the proposed method is independent of parameter variations. Because of the independency of this method against the parameter variations, it works similarly to the search based efficiency control methods. Two DSP boards including estimator and controller are used to achieve high accuracy and speed in estimating and controlling machines. The simulation and experimental results verify the robustness of the sensorless method against parameter variations.
- Subjects :
- Model control
General Computer Science
Computer science
business.industry
020209 energy
020208 electrical & electronic engineering
General Engineering
Estimator
02 engineering and technology
Kalman filter
loss model control
Extended Kalman filter
six-phase induction motor
EKF
Control theory
Robustness (computer science)
FOC
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Induction motor
Digital signal processing
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....013360869709887eba1bd2e83d37a1d1