Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data
Abstract
:1. Introduction
2. Methodology
2.1. Technical Route
2.2. Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data
2.2.1. Feature Extraction
2.2.2. The GAN-Based Model for Generating Samples of Abnormal Line-Transformer Relationship
2.2.3. Support Vector Machine
3. Experimental Results and Analysis
3.1. Data Description
3.2. Data Preprocessing
3.3. Feature Extraction
3.4. Generating Samples of Abnormal Line-Transformer Relationship Based on GAN
3.5. Build the Classifier Model
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Number | Number |
---|---|---|
Line hanging error | 12 | 1 |
Magnification error | 42 | 2 |
Normal | 395 | 3 |
Total | 449 | / |
Data Type | Daily Electricity Data. Unit: kwh | Category | |||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | … | 30 | |||
Input power of line A | 68,800 | 69,200 | 62,800 | … | 42,800 | / | |
Power consumption of each transformer | transformer 1 | 273 | 263 | 282 | … | 279 | Normal |
transformer 2 | 77 | 77 | 76 | … | 75 | Line hanging error | |
… | … | … | … | … | … | … | |
transformer 22 | 320 | 316 | 400 | … | 524 | Magnification error | |
The power loss of line A | 65,460 | 65,485 | 59,148 | … | 38,542 | / |
Feature | Transformer 1 | Transformer 2 | Transformer 3 | … | Transformer 449 |
---|---|---|---|---|---|
0.24414 | −0.17513 | 0.27490 | … | 0.48841 | |
0.24992 | −0.18690 | 0.27370 | … | 0.48176 | |
0.12639 | 7.30734 | 0.69886 | … | 0.41743 | |
0.12216 | 6.86262 | 0.65633 | … | 0.39203 | |
−0.00175 | 0.00026 | −0.00016 | … | 0.00094 | |
−0.02953 | 0.00087 | 0.00164 | … | 0.01527 | |
−0.00023 | −0.00002 | 0.00002 | … | 0.00028 | |
0.00019 | 0.00692 | 0.00058 | … | 0.00064 | |
0.00064 | −0.00015 | 0.00088 | … | 0.00114 | |
−0.00022 | −0.00001 | 0.00002 | … | 0.00027 | |
0.00018 | 0.00676 | 0.00057 | … | 0.00062 | |
0.00064 | −0.00015 | 0.00088 | … | 0.00115 |
Line Hanging Error | Magnification Error | Normal | mGM | |
---|---|---|---|---|
SMOTE | 86.05% | 83.87% | 77.46% | 82.38% |
GAN | 97.32% | 92.27% | 98.10% | 95.86% |
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Wang, Y.; Zhang, X.; Liu, H.; Li, B.; Yu, J.; Liu, K.; Qin, L. Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data. Sustainability 2022, 14, 8611. https://doi.org/10.3390/su14148611
Wang Y, Zhang X, Liu H, Li B, Yu J, Liu K, Qin L. Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data. Sustainability. 2022; 14(14):8611. https://doi.org/10.3390/su14148611
Chicago/Turabian StyleWang, Yan, Xinyu Zhang, Haofeng Liu, Boqiang Li, Jinyun Yu, Kaipei Liu, and Liang Qin. 2022. "Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data" Sustainability 14, no. 14: 8611. https://doi.org/10.3390/su14148611
APA StyleWang, Y., Zhang, X., Liu, H., Li, B., Yu, J., Liu, K., & Qin, L. (2022). Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data. Sustainability, 14(14), 8611. https://doi.org/10.3390/su14148611