A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM
Abstract
:1. Introduction
2. Related Theory
2.1. Kernel Principal Component Analysis
2.2. Differential Evolution
2.2.1. Initialization of Population
2.2.2. Mutation
2.2.3. Crossover
2.2.4. Selection
2.3. Grey Wolf Optimizer
2.3.1. Social Hierarchy
2.3.2. Encircling Prey
2.3.3. Hunting
2.3.4. Attacking Prey
2.3.5. Searching Prey
Algorithm 1. GWO pseudo-code |
(1) Initialize the positions of grey wolf population Xi (i = 1,2,3…, n) randomly. (2) Initialize a, A, C. (3) Find α, β, and δ as the first three best solutions based on their fitness values. (4) t = 0. while t ≤ MaxIter do for each Wolfi pack do Update current wolf’s position according to Equation (15). end - Update a, A, and C as in Equations (16), (11) and (12). - Evaluate the positions of individual wolves. - Update α, β, and δ positions as the first best three solutions in the current population. - t = t + 1. end (5) Select the optimal grey wolf position. |
2.4. Least Square Support Vector Machine
3. Fault Diagnosis Model Based on HGWO-LSSVM
4. Case study and Analysis
4.1. Fault Sample Collection
4.2. Feature Set Selection
4.3. Multi-Class Classification Model
4.4. Results and Discussion
4.4.1. Example 1
4.4.2. Example 2
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Fault Type | Total | N | T1 | T2 | T3 | PD | D1 | D2 | |
---|---|---|---|---|---|---|---|---|---|
Voltage Level | |||||||||
110 kV | 244 | 56 | 0 | 16 | 121 | 1 | 8 | 42 | |
220 kV | 734 | 184 | 2 | 63 | 222 | 55 | 63 | 145 | |
500 kV | 191 | 30 | 1 | 12 | 54 | 8 | 57 | 29 | |
750 kV | 112 | 10 | 2 | 1 | 0 | 0 | 5 | 94 | |
Total | 1281 | 280 | 5 | 92 | 397 | 64 | 133 | 310 |
No. | H2 | CH4 | C2H2 | C2H4 | C2H6 | CO | CO2 | Actual Fault | IEC Ratio |
---|---|---|---|---|---|---|---|---|---|
1 | 96 | 20.61 | 38.57 | 15.82 | 5.4 | 367 | 854 | D1 | D2 |
2 | 89 | 20.01 | 39.4 | 16.36 | 6.16 | 354 | 874 | D1 | D2 |
3 | 134.78 | 34.7 | 94.1 | 40.54 | 4.5 | 53.1 | 89.76 | D2 | D2 |
4 | 207.6 | 44.14 | 139 | 80.9 | 3.8 | 29.62 | 331.7 | D2 | D2 |
5 | 292.58 | 38.39 | 0 | 0.84 | 3.87 | 161.54 | 523.68 | PD | N |
6 | 522.2 | 43.21 | 1.01 | 1.02 | 16.73 | 158.6 | 2251.3 | PD | D2 |
7 | 529.75 | 58.96 | 1.27 | 5.06 | 18.12 | 160.5 | 2263.98 | PD | D2 |
8 | 2525.3 | 130.55 | 0 | 1.53 | 14.25 | 612.17 | 2687.13 | PD | PD |
9 | 3417.62 | 131.42 | 0 | 1.22 | 14.36 | 428.03 | 2770.29 | PD | PD |
10 | 5869.58 | 175.21 | 0 | 1.45 | 16.45 | 624.47 | 3684.56 | PD | PD |
11 | 4966.14 | 145.66 | 0 | 1.28 | 15.33 | 503.42 | 3397.51 | PD | PD |
12 | 6.82 | 10.13 | 0 | 74.85 | 3.81 | 662.43 | 5871.86 | T2 | T3 |
13 | 14 | 33.3 | 0 | 20.1 | 8 | 101 | 654 | T2 | T2 |
14 | 87 | 223.6 | 0 | 121.1 | 49.6 | 62 | 498 | T2 | T2 |
15 | 78 | 196.3 | 0 | 109.3 | 46.1 | 51 | 384 | T2 | T2 |
16 | 22.04 | 171.05 | 0 | 182.04 | 91.29 | 1651.57 | 16,390.39 | T2 | T2 |
17 | 82.74 | 108.92 | 3.91 | 249.8 | 28.06 | 809.04 | 2053.72 | T3 | T3 |
18 | 3.11 | 6.61 | 0.26 | 36.43 | 3.23 | 296.54 | 2367.99 | T3 | T3 |
19 | 3.05 | 5.84 | 0.27 | 37.28 | 3.38 | 256.61 | 2970.88 | T3 | T3 |
20 | 3.82 | 7.93 | 0.13 | 52.68 | 3.37 | 406.24 | 2770.54 | T3 | T3 |
Feature Set | Content | |
---|---|---|
DGA gases | Total | H2, CH4, C2H2, C2H4, C2H6, CO, CO2 |
Common | H2, CH4, C2H2, C2H4, C2H6 | |
DGA gas ratios | Doernenberg | CH4/H2, C2H2/C2H4, C2H2/CH4, C2H6/C2H2 |
Roger | C2H6/CH4, C2H2/C2H4, CH4/H2, C2H4/C2H6 | |
IEC 60599 | C2H2/C2H4, CH4/H2, C2H4/C2H6 | |
CIGRE gas ratio | C2H2/C2H6, H2/CH4, C2H4/C2H6, C2H2/H2, CO/CO2 |
Model | Training Sample | Test Sample | Classification Accuracy (%) | C | σ | Training Time (ms) |
---|---|---|---|---|---|---|
LS-SVM1 | 900 | 381 | 98.7 | 5.8263 | 1.8523 | 3264 |
LS-SVM2 | 700 | 299 | 97.0 | 2.3560 | 2.6530 | 2646 |
LS-SVM3 | 346 | 148 | 96.62 | 3.6382 | 1.8635 | 2235 |
LS-SVM4 | 355 | 152 | 97.37 | 1.8693 | 0.9685 | 2024 |
LS-SVM5 | 310 | 123 | 97.56 | 1.0635 | 0.8625 | 1956 |
Method | IEC Three-Ratio | Rogers Ratio | Duval Triangle | Dornenburg Ratio | LSSVM | HGWO-LSSVM |
---|---|---|---|---|---|---|
Accuracy Rate | 75.41% | 63.84% | 73.73% | 53.26% | 88.75% | 97.45% |
Model | Average | Classification Accuracy | Training Time (ms) | |
---|---|---|---|---|
Upper Limit(%) | Lower Limit(%) | |||
LSSVM | 88.75 | 90.25 | 86.75 | 3654 |
PSO-LSSVM | 89.38 | 91.6 | 87.16 | 3562 |
GA-LSSVM | 92.25 | 92.6 | 91.9 | 4526 |
GWO-LSSVM | 94.6 | 95.25 | 93.95 | 2615 |
HGWO-LSSVM | 97.45 | 98.7 | 96.62 | 2135 |
Model | Classification Accuracy (%) | |
---|---|---|
Dissolved Gases Concentration | OHFS | |
LSSVM | 80.35 | 90.25 |
PSO-LSSVM | 82.63 | 91.6 |
GA-LSSVM | 83.55 | 92.6 |
GWO-LSSVM | 85.28 | 95.25 |
HGWO-LSSVM | 87.53 | 98.7 |
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Zeng, B.; Guo, J.; Zhu, W.; Xiao, Z.; Yuan, F.; Huang, S. A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM. Energies 2019, 12, 4170. https://doi.org/10.3390/en12214170
Zeng B, Guo J, Zhu W, Xiao Z, Yuan F, Huang S. A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM. Energies. 2019; 12(21):4170. https://doi.org/10.3390/en12214170
Chicago/Turabian StyleZeng, Bing, Jiang Guo, Wenqiang Zhu, Zhihuai Xiao, Fang Yuan, and Sixu Huang. 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM" Energies 12, no. 21: 4170. https://doi.org/10.3390/en12214170
APA StyleZeng, B., Guo, J., Zhu, W., Xiao, Z., Yuan, F., & Huang, S. (2019). A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM. Energies, 12(21), 4170. https://doi.org/10.3390/en12214170