Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1111-1120 |
| Number of pages | 10 |
| Journal | Electric Power Systems Research |
| Volume | 80 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - Sep 2010 |
| Externally published | Yes |
Fingerprint
Keywords
- Bacterial swarming algorithm
- Frequency response analysis
- Mathematical model
- Parameter identification
- Transformer winding
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Cite this
Improved modelling of power transformer winding using bacterial swarming algorithm and frequency response analysis. / Shintemirov, A.; Tang, W. J.; Tang, W. H.; Wu, Q. H.
In: Electric Power Systems Research, Vol. 80, No. 9, 09.2010, p. 1111-1120.Research output: Contribution to journal › Article
}
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.
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
UR - http://www.scopus.com/inward/record.url?scp=80054088181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054088181&partnerID=8YFLogxK
U2 - 10.1016/j.epsr.2010.03.001
DO - 10.1016/j.epsr.2010.03.001
M3 - Article
VL - 80
SP - 1111
EP - 1120
JO - Electric Power Systems Research
JF - Electric Power Systems Research
SN - 0378-7796
IS - 9
ER -