A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm
Abstract
:1. Introduction
2. Graph Neural Network
2.1. Graph Convolutional Neural Network
2.2. GraphSAGE Algorithm
3. Database Construction
3.1. Fault Simulation in Power Systems
3.2. Graph Structure Construction
4. Fault Diagnosis and Accurate Localization Model of Power System Based on GraphSAGE Algorithm
5. Validation Experiments
5.1. Diagnosis Model Validation
5.2. Accurate Localization Model Validation
6. Conclusions
- (1)
- While some studies have used GCN, GAT, classical deep learning (CNN), and machine learning (Bayesian network) methods to address fault type classification and fault interval localization in power grids. Few studies have also established an AI-based model for accurate fault localization. This article introduces an innovative approach by using GraphSAGE to build an accurate fault localization model, leveraging its inductive learning capability and fully utilizing topological information to realize accurate fault localization;
- (2)
- Extensive validation data demonstrate that the higher the degree of a node, the more effectively it can learn features from neighboring nodes. This property allows for the identification of key nodes when processing large-scale power grid data, thereby reducing the complexity of the model while maintaining accuracy;
- (3)
- In simulations involving topology changes in the power grid, GraphSAGE consistently outperforms GCN and GAT, with an accuracy improvement of about 3% and 2%. This demonstrates its strong adaptability to topology variations and excellent practical applicability;
- (4)
- In the accurate localization model, the random sampling characteristic and inductive learning capability of the GraphSAGE algorithm can achieve significantly higher localization accuracy compared to GAT and GCN;
- (5)
- When the line length does not exceed 7.5 km, this method offers certain advantages over the traveling wave method. Moreover, this method offers greater economic practicality compared to the traveling wave method.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter Settings | |
---|---|
Fault type | single-phase grounding (A-phase, B-phase, C-phase), two-phase short circuit (AB-phase, BC-phase, AC-phase), two-phase grounding (AB-phase, BC-phase, AC-phase), three-phase short circuit |
Fault point position | 10%, 20%, …, 90% |
Load level | 80%, 85%, …, 120% |
Total sampling duration | 40 cycles |
Sampling step size | 0.1 cycles |
Fault start time | 10th cycle |
Fault end time | 15th cycle |
Types of sampling data | three-phase voltage amplitude three-phase voltage phase angle |
Topological structure changes | connection relationship between bus1–bus2 changes to bus2–bus3 add connection relationship bus19–bus23 |
Models | GraphSAGE | GCN | GAT | CNN | Bayesian Network |
---|---|---|---|---|---|
Complete dataset | 98.59% | 97.15% | 98.08% | 95.38% | 91.32% |
Loss of 4 nodes | 97.35% | 93.13% | 97.25% | 92.33% | 85.93% |
Loss of 8 nodes | 95.32% | 92.81% | 95.24% | 89.42% | 80.89% |
Loss of 12 nodes | 95.13% | 90.07% | 91.89% | 84.5% | 71.01% |
Loss of 16 nodes | 94.67% | 86.42% | 90.57% | 79.18% | 60.83% |
Loss of 20 nodes | 92.19% | 83.36% | 86.42% | 73.49% | 56.52% |
Loss of 24 nodes | 89.79% | 80.24% | 85.35% | 66.31 | 52.13% |
Connection relationship changes from bus26–bus28 to bus27–bus28 | 97.86% | 94.75% | 96.37% | 91.42% | 86.13% |
Add connection relationship bus19–bus23 | 97.63% | 94.28% | 95.84% | 91.14% | 84.75% |
Range | Number | Proportion |
---|---|---|
[0, 0.5%) | 185 | 6.72% |
[0.5%, 1%) | 552 | 20.04% |
[1%, 1.5%) | 639 | 23.20% |
[1.5%, 2%) | 784 | 28.47% |
[2%, 2.5%) | 335 | 12.16% |
[2.5%, 3%) | 201 | 7.30% |
[3%, 3.5%) | 45 | 1.63% |
[3.5%, 4%) | 13 | 0.47% |
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Wang, F.; Hu, Z. A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm. Electronics 2025, 14, 1219. https://doi.org/10.3390/electronics14061219
Wang F, Hu Z. A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm. Electronics. 2025; 14(6):1219. https://doi.org/10.3390/electronics14061219
Chicago/Turabian StyleWang, Fang, and Zhijian Hu. 2025. "A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm" Electronics 14, no. 6: 1219. https://doi.org/10.3390/electronics14061219
APA StyleWang, F., & Hu, Z. (2025). A Novel Fault Diagnosis and Accurate Localization Method for a Power System Based on GraphSAGE Algorithm. Electronics, 14(6), 1219. https://doi.org/10.3390/electronics14061219