Abstract
This paper reviews and compares available techniques for minimizing power loss of induction motors. It identifies a separate category of loss minimization techniques (LMTs)hybrid LMTs. Techniques have different convergence rates, parameter dependence, and convergence errors. Offline techniques are introduced and real-time techniques are detailed. Real-time LMTs are divided into model-based, physics-based, and hybrid categories. Application of a real-time LMT to a hybrid electric vehicle (HEV) is examined. Simulation and experimental results verify the characteristics of each category of real-time LMTs. Sensitivity of real-time LMTs to motor parameters is discussed. It is shown that techniques can be chosen based on application-specific requirements and that hybrid LMTs balance partial parameter independence and fast convergence to achieve the best overall performance.
| Original language | English (US) |
|---|---|
| Article number | 5565477 |
| Pages (from-to) | 2319-2328 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Industry Applications |
| Volume | 46 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 1 2010 |
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Keywords
- Control-based optimization
- induction motor losses
- loss minimization
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering
Cite this
Review of methods for real-time loss minimization in induction machines. / Bazzi, Ali M.; Krein, Philip T.
In: IEEE Transactions on Industry Applications, Vol. 46, No. 6, 5565477, 01.11.2010, p. 2319-2328.Research output: Contribution to journal › Review article
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TY - JOUR
T1 - Review of methods for real-time loss minimization in induction machines
AU - Bazzi, Ali M.
AU - Krein, Philip T
PY - 2010/11/1
Y1 - 2010/11/1
N2 - This paper reviews and compares available techniques for minimizing power loss of induction motors. It identifies a separate category of loss minimization techniques (LMTs)hybrid LMTs. Techniques have different convergence rates, parameter dependence, and convergence errors. Offline techniques are introduced and real-time techniques are detailed. Real-time LMTs are divided into model-based, physics-based, and hybrid categories. Application of a real-time LMT to a hybrid electric vehicle (HEV) is examined. Simulation and experimental results verify the characteristics of each category of real-time LMTs. Sensitivity of real-time LMTs to motor parameters is discussed. It is shown that techniques can be chosen based on application-specific requirements and that hybrid LMTs balance partial parameter independence and fast convergence to achieve the best overall performance.
AB - This paper reviews and compares available techniques for minimizing power loss of induction motors. It identifies a separate category of loss minimization techniques (LMTs)hybrid LMTs. Techniques have different convergence rates, parameter dependence, and convergence errors. Offline techniques are introduced and real-time techniques are detailed. Real-time LMTs are divided into model-based, physics-based, and hybrid categories. Application of a real-time LMT to a hybrid electric vehicle (HEV) is examined. Simulation and experimental results verify the characteristics of each category of real-time LMTs. Sensitivity of real-time LMTs to motor parameters is discussed. It is shown that techniques can be chosen based on application-specific requirements and that hybrid LMTs balance partial parameter independence and fast convergence to achieve the best overall performance.
KW - Control-based optimization
KW - induction motor losses
KW - loss minimization
UR - http://www.scopus.com/inward/record.url?scp=78649391192&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649391192&partnerID=8YFLogxK
U2 - 10.1109/TIA.2010.2070475
DO - 10.1109/TIA.2010.2070475
M3 - Review article
AN - SCOPUS:78649391192
VL - 46
SP - 2319
EP - 2328
JO - IEEE Transactions on Industry Applications
JF - IEEE Transactions on Industry Applications
SN - 0093-9994
IS - 6
M1 - 5565477
ER -