TY - JOUR
T1 - Improved modelling of power transformer winding using bacterial swarming algorithm and frequency response analysis
AU - Shintemirov, A.
AU - Tang, W. J.
AU - Tang, W. H.
AU - Wu, Q. H.
N1 - Funding Information:
The first author would like to thank the Center for International Programs for granting Kazakhstan Presidential Bolashak Scholarship to support his PhD research in the University of Liverpool, UK. He is also indebted to Dr. N. Abeywickrama and Professor S. M. Gubanski from the Chalmers University of Technology, Göteborg, Sweden, for providing experimental data.
PY - 2010/9
Y1 - 2010/9
N2 - The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which is used for the subsequent model parameters identification with a novel BSA. The newly introduced BSA, being developed upon a bacterial foraging behavior, is described in detail. Simulations and discussions are presented to explore the potential of the proposed approach using simulated and experimentally measured FRA responses taken from two transformers. The BSA identification results are compared with those using genetic algorithm. It is shown that the proposed BSA delivers satisfactory parameter identification and improved modelling can be used for FRA results interpretation.
AB - The paper discusses an improved modelling of transformer windings based on bacterial swarming algorithm (BSA) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which is used for the subsequent model parameters identification with a novel BSA. The newly introduced BSA, being developed upon a bacterial foraging behavior, is described in detail. Simulations and discussions are presented to explore the potential of the proposed approach using simulated and experimentally measured FRA responses taken from two transformers. The BSA identification results are compared with those using genetic algorithm. It is shown that the proposed BSA delivers satisfactory parameter identification and improved modelling can be used for FRA results interpretation.
KW - Bacterial swarming algorithm
KW - Frequency response analysis
KW - Mathematical model
KW - Parameter identification
KW - Transformer winding
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U2 - 10.1016/j.epsr.2010.03.001
DO - 10.1016/j.epsr.2010.03.001
M3 - Article
AN - SCOPUS:80054088181
VL - 80
SP - 1111
EP - 1120
JO - Electric Power Systems Research
JF - Electric Power Systems Research
SN - 0378-7796
IS - 9
ER -