Transformer Parameters Estimation From Nameplate Data Using Evolutionary Programming Techniques
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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.
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