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Load extrapolation computational algorithms for energy efficiency measurement testing of high‐power and high‐voltage induction motors.

Authors :
Yin, Xiao‐Dong
Lan, Lei
Xiong, Bo
Source :
IEEJ Transactions on Electrical & Electronic Engineering. Oct2019, Vol. 14 Issue 10, p1570-1574. 5p.
Publication Year :
2019

Abstract

This paper deals with load extrapolation methods and algorithms for the estimation of the energy efficiency of induction motors, starting from the electrical data generally available after parameter identification. The main aim of this paper is to help the readers move from the theoretical analysis to the experimental verification, which is suitable to be included in its own testing. For this reason, step‐by‐step algorithms are included in this paper. The first part deals with the determination of energy efficiency based on an original load extrapolation. Finally, experimental verification of the high‐power and high‐voltage induction motors is provided. Comparison between the values of the in–out method and load extrapolation method confirms the validity of the proposed algorithms. Compared to the direct in–out method, the error of the load extrapolation proposed in this paper is less than 1%.The load extrapolation method is mainly used for the high‐voltage and high‐power asynchronous motor. The high‐voltage and high‐power motor can be used to drive a low‐power motor to achieve coaxial twin trawling in the absence of a suitable power supply and matched load. The energy efficiency of a high‐power motor under different loads are measured and estimated using a mathematical fitting method. The estimated energy efficiency curves in the whole load range can be used to evaluate the high‐power and high‐voltage induction motor. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19314973
Volume :
14
Issue :
10
Database :
Academic Search Index
Journal :
IEEJ Transactions on Electrical & Electronic Engineering
Publication Type :
Academic Journal
Accession number :
138540640
Full Text :
https://doi.org/10.1002/tee.22977