Abstract
This paper presents a methodology for assessing the reliability of thermal-model parameters for transformers estimated from measured data. The methodology uses statistical bootstrapping to calculate confidence levels (CL) and confidence intervals (CI). Bootstrapping allows us to make a small dataset look statistically larger, which allows a precise estimate of the transformer thermal model's reliability. The proposed methodology is tested on a 167-MVA oil-forced air-forced transformer. The CIs are evaluated with and without bootstrapping and the reliability indices are compared. The results show that the CI and CL values with bootstrapping are more consistently reproducible than the ones derived without bootstrapping.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 169-176 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Power Delivery |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2009 |
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Keywords
- Bootstrapping
- Confi-dence levels (CLs)
- Confidence intervals (CIs)
- Least squares regression
- Parameter estimation
- Top-oil temperature
- Transformer thermal modeling
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Energy Engineering and Power Technology
Cite this
Improving reliability assessment of transformer thermal top-oil model parameters estimated from measured data. / Jauregui-Rivera, Lida; Mao, Xiaolin; Tylavsky, Daniel.
In: IEEE Transactions on Power Delivery, Vol. 24, No. 1, 2009, p. 169-176.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improving reliability assessment of transformer thermal top-oil model parameters estimated from measured data
AU - Jauregui-Rivera, Lida
AU - Mao, Xiaolin
AU - Tylavsky, Daniel
PY - 2009
Y1 - 2009
N2 - This paper presents a methodology for assessing the reliability of thermal-model parameters for transformers estimated from measured data. The methodology uses statistical bootstrapping to calculate confidence levels (CL) and confidence intervals (CI). Bootstrapping allows us to make a small dataset look statistically larger, which allows a precise estimate of the transformer thermal model's reliability. The proposed methodology is tested on a 167-MVA oil-forced air-forced transformer. The CIs are evaluated with and without bootstrapping and the reliability indices are compared. The results show that the CI and CL values with bootstrapping are more consistently reproducible than the ones derived without bootstrapping.
AB - This paper presents a methodology for assessing the reliability of thermal-model parameters for transformers estimated from measured data. The methodology uses statistical bootstrapping to calculate confidence levels (CL) and confidence intervals (CI). Bootstrapping allows us to make a small dataset look statistically larger, which allows a precise estimate of the transformer thermal model's reliability. The proposed methodology is tested on a 167-MVA oil-forced air-forced transformer. The CIs are evaluated with and without bootstrapping and the reliability indices are compared. The results show that the CI and CL values with bootstrapping are more consistently reproducible than the ones derived without bootstrapping.
KW - Bootstrapping
KW - Confi-dence levels (CLs)
KW - Confidence intervals (CIs)
KW - Least squares regression
KW - Parameter estimation
KW - Top-oil temperature
KW - Transformer thermal modeling
UR - http://www.scopus.com/inward/record.url?scp=58249130248&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58249130248&partnerID=8YFLogxK
U2 - 10.1109/TPWRD.2008.2005686
DO - 10.1109/TPWRD.2008.2005686
M3 - Article
AN - SCOPUS:58249130248
VL - 24
SP - 169
EP - 176
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
SN - 0885-8977
IS - 1
ER -