A Novel Integrated Method to Diagnose Faults in Power Transformers
Abstract
:1. Introduction
2. TFD Model Based on SVM Integrated with PCA and PSO
2.1. Data Set Preprocessed by PCA
2.2. Support Vector Machine
2.3. Parameter Optimization in SVM Using Improved PSO
3. Verification and Discussion
3.1. TFD Example 1
3.2. TFD Example 2
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Fault Type | Training Sample | Test Sample | Total |
---|---|---|---|
Normal | 17 | 7 | 24 |
LE-D | 23 | 10 | 33 |
HE-D | 20 | 8 | 28 |
HT | 23 | 10 | 33 |
MT | 7 | 2 | 9 |
ML-T | 13 | 5 | 18 |
LT | 9 | 3 | 12 |
Total | 112 | 45 | 157 |
Method | Three-Ratio | Duval Triangle | BPNN | SVM | This Paper |
---|---|---|---|---|---|
Accuracy rate | 51.111% | 42.222% | 60.000% | 75.556% | 93.333% |
Types | Data | C | Accuracy Rate | |
---|---|---|---|---|
1 | Dissolved gas data in oil only | 5.023 | 0.709931 | 80% |
2 | SCADA data only | 26.5631 | 7.3787 | 65% |
3 | Both above data | 36.6918 | 0.074581 | 95% |
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Share and Cite
Wu, J.; Li, K.; Sun, J.; Xie, L. A Novel Integrated Method to Diagnose Faults in Power Transformers. Energies 2018, 11, 3041. https://doi.org/10.3390/en11113041
Wu J, Li K, Sun J, Xie L. A Novel Integrated Method to Diagnose Faults in Power Transformers. Energies. 2018; 11(11):3041. https://doi.org/10.3390/en11113041
Chicago/Turabian StyleWu, Jing, Kun Li, Jing Sun, and Li Xie. 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers" Energies 11, no. 11: 3041. https://doi.org/10.3390/en11113041