A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements
Abstract
:1. Introduction
2. Materials and Methods
2.1. Fault Recognition Algorithm (FRA) Database
2.2. Fault Recognition Algorithm (FRA) Data Preliminaries
2.3. Fault Recognition Algorithm (FRA) Measuring Frequency Discretisation
- The FRA data of investigated transformers with known faults were compared with previous FRA data of the same unit.
- In the case where the fingerprint of the same unit was not available, the latest FRA data was compared with the same MVA unit designed according to the same technical specification.
- The FRA data of one phase were compared with those of another phase of the same unit.
2.4. Development of Regression Analysis Model
2.5. Numerical Indicators Benchmarking
2.6. Performance Analysis
3. Results
3.1. Measurement Setup
3.2. Case Studies
3.2.1. Investigating Frequency Response of Transformers in Good Condition
Case Study 1
3.2.2. Investigating Frequency Response for Transformers with Winding Damages
Case Study 2
Case Study 3
Case Study 4
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Frequency Region | Transformer Component | Influencing Elements |
---|---|---|
1–10 kHz | Main core Winding inductance | Core deformation, open circuits, shorted turns and residual magnetism |
10–100 kHz | Bulk component Main windings | Deformation within the main or tap windings Bulk winding movement between windings and clamping structure |
400 kHz–1 MHz | Main windings Tap windings Internal leads | Movement of the main and tap windings, ground impedance variations |
Case # | Location | Power Rating | Voltage Ratings |
---|---|---|---|
1 | Gauteng | 20 MVA | 132/11 kV |
2 | Gauteng | 50 MVA | 66/11.66 kV |
3 | North West | 40 MVA | 132/11 kV |
4 | North West | 10 MVA | 66/11 kV |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 1 | 1 | 1 |
ASLE | 0 | 0 | 0 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 0 | 0 | 0 |
R2 | 1 | 1 | 1 |
Adjusted R2 | 1 | 1 | 1 |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 0.998 | 0.8909 | 0.9981 |
ASLE | 0.02 | 0.3330 | 0.034 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 4.242 | 19.789 | 17.615 |
R2 | 0.997 | 0.879 | 0.987 |
Adjusted R2 | 0.997 | 0.878 | 0.987 |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 0.5057 | 0.9121 | 0.9798 |
ASLE | 1 | 0.1734 | 0.0475 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 26.858 | 3.12 | 1.351 |
R2 | 0.251 | 0.935 | 0.951 |
Adjusted R2 | 0.248 | 0.935 | 0.838 |
Frequency Region | Equation of the Model |
---|---|
Low (1–10 kHz) | LSD = 147.0374 + 2.2869 × Fingerprint |
Medium (10–100 kHz) | LSD = −4.3357 + 0.9963 × Fingerprint |
High (100 kHz–1 MHz) | LSD = 3.3804 + 0.9187 × Fingerprint |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 0.999 | 0.9191 | 0.0921 |
ASLE | 0.03 | 0.9632 | 0.125 |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 0.999 | 0.8804 | 0.9316 |
ASLE | 0.0493 | 0.346 | 0.432 |
Technique | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
CC | 1.000 | 0.9597 | 0.9865 |
ASLE | 0.0225 | 0.9823 | 0.536 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 0.256 | 12.176 | 8.236 |
R2 | 0.999 | 0.834 | 0.840 |
Adjusted R2 | 0.999 | 0.833 | 0.839 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 0.064 | 10.935 | 6.907 |
R2 | 0.999 | 0.761 | 0.861 |
Adjusted R2 | 0.999 | 0.760 | 0.860 |
The Goodness of Fit Statistics | Frequency Region | ||
---|---|---|---|
Low (1–10 kHz) | Medium (10–100 kHz) | High (100 kHz–1 MHz) | |
Std. deviation | 0.280 | 12.744 | 8.457 |
R2 | 1.000 | 0.957 | 0.972 |
Adjusted R2 | 1.000 | 0.957 | 0.972 |
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Thango, B.A.; Nnachi, A.F.; Dlamini, G.A.; Bokoro, P.N. A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements. Energies 2022, 15, 2335. https://doi.org/10.3390/en15072335
Thango BA, Nnachi AF, Dlamini GA, Bokoro PN. A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements. Energies. 2022; 15(7):2335. https://doi.org/10.3390/en15072335
Chicago/Turabian StyleThango, Bonginkosi A., Agha F. Nnachi, Goodness A. Dlamini, and Pitshou N. Bokoro. 2022. "A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements" Energies 15, no. 7: 2335. https://doi.org/10.3390/en15072335
APA StyleThango, B. A., Nnachi, A. F., Dlamini, G. A., & Bokoro, P. N. (2022). A Novel Approach to Assess Power Transformer Winding Conditions Using Regression Analysis and Frequency Response Measurements. Energies, 15(7), 2335. https://doi.org/10.3390/en15072335