Abstract
Quite often because of the complexity in the design of large industrial motors, the theoretical motor parameter calculations do not match actual results from laboratory tests. Thus, it becomes important to predict the amount of discrepancy between the two methods to develop confidence in the motor parameter calculations. This paper discusses the development of multiple artificial neural networks (ANNs) designed to predict the ratios of measured parameters to calculated parameters, given the geometry and construction of the motor. These ratios represent correction factors which can be applied to the values calculated from the theoretical program, which, in this case, is a software package known as MPE program.
Recommended Citation
H. Hirlekar et al., "Reconciling Motor Performance Indicators from Theoretical Calculations and Laboratory Tests," North American Power Symposium 2010, NAPS 2010, article no. 5618949, Institute of Electrical and Electronics Engineers, Dec 2010.
The definitive version is available at https://doi.org/10.1109/NAPS.2010.5618949
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Artificial neural networks; Backpropagation; Feedforward network; Mean square error; Motor performance estimation
International Standard Book Number (ISBN)
978-142448046-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
17 Dec 2010
