Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques

This paper proposes a simple and effective evolutionarycomputation-based technique to estimate the equivalentcircuit parameters of a single-phase transformer from its nameplatedata without the need to conduct any experimental measurements.Two techniques, namely: particle swarm optimizationand geneti...

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Main Authors: Mossad, M., Azab, M., Abu-Siada, Ahmed
Format: Journal Article
Published: IEEE Power Engineering Society 2014
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/17014
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author Mossad, M.
Azab, M.
Abu-Siada, Ahmed
author_facet Mossad, M.
Azab, M.
Abu-Siada, Ahmed
author_sort Mossad, M.
building Curtin Institutional Repository
collection Online Access
description This paper proposes a simple and effective evolutionarycomputation-based technique to estimate the equivalentcircuit parameters of a single-phase transformer from its nameplatedata without the need to conduct any experimental measurements.Two techniques, namely: particle swarm optimizationand genetic algorithm are employed to track nameplate data byminimizing certain objective functions. The effectiveness of theproposed technique is examined through its application for threesingle-phase transformers of different ratings. The results showthat evolutionary computation techniques can precisely identifytransformer equivalent circuit parameters. The proposed techniquecan be extended to estimate the parameters of a three-phasepower transformer from its nameplate data without taking thetransformer out of service to carry out any experimental testing.
first_indexed 2025-11-14T07:19:25Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:19:25Z
publishDate 2014
publisher IEEE Power Engineering Society
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-170142017-09-13T15:44:54Z Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques Mossad, M. Azab, M. Abu-Siada, Ahmed particle swarm Genetic algorithm (GA) transformer parameters estimation This paper proposes a simple and effective evolutionarycomputation-based technique to estimate the equivalentcircuit parameters of a single-phase transformer from its nameplatedata without the need to conduct any experimental measurements.Two techniques, namely: particle swarm optimizationand genetic algorithm are employed to track nameplate data byminimizing certain objective functions. The effectiveness of theproposed technique is examined through its application for threesingle-phase transformers of different ratings. The results showthat evolutionary computation techniques can precisely identifytransformer equivalent circuit parameters. The proposed techniquecan be extended to estimate the parameters of a three-phasepower transformer from its nameplate data without taking thetransformer out of service to carry out any experimental testing. 2014 Journal Article http://hdl.handle.net/20.500.11937/17014 10.1109/TPWRD.2014.2311153 IEEE Power Engineering Society restricted
spellingShingle particle swarm
Genetic algorithm (GA)
transformer
parameters estimation
Mossad, M.
Azab, M.
Abu-Siada, Ahmed
Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title_full Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title_fullStr Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title_full_unstemmed Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title_short Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
title_sort transformer parameters estimation from nameplate data using evolutionary programming techniques
topic particle swarm
Genetic algorithm (GA)
transformer
parameters estimation
url http://hdl.handle.net/20.500.11937/17014