A graph neural network based fault diagnosis strategy for power communication networks

References

• Agunbiade, O. Y., S. M. Ngwira, T. Zuva, and Y. Akanbi. 2016. “Improving Ground Detection for Unmanned Vehicle Systems in Environmental Noise Scenarios.” The International Journal of Advanced Manufacturing Technology 84 (9):2719–2727. doi:10.1007/s00170-015-8109-8.
   Web of Science ®Google Scholar
• Benkercha, R., and S. Moulahoum. 2018. “Fault Detection and Diagnosis Based on C4.5 Decision Tree Algorithm for Grid Connected PV System.” Solar Energy 173:610–634. doi:10.1016/j.solener.2018.07.089.
   Web of Science ®Google Scholar
• Cai, B. P., H. L. Liu, and M. Xie. 2016. “A Real-Time Fault Diagnosis Methodology of Complex Systems Using Object-Oriented Bayesian Networks.” Mechanical Systems and Signal Processing 80: 31–44. doi:10.1016/j.ymssp.2016.04.019.
   Web of Science ®Google Scholar
• Chen, Y. Q., O. Fink, and G. Sansavini. 2018. “Combined Fault Location and Classification for Power Transmission Lines Fault Diagnosis with Integrated Feature Extraction.” IEEE Transactions on Industrial Electronics 65 (1): 561–569. doi:10.1109/TIE.2017.2721922.
   Web of Science ®Google Scholar
• Chen, K. J., J. Hu, Y. Zhang, Z. Q. Yu, and J. L. He. 2020. “Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks.” IEEE Journal on Selected Areas in Communications 38 (1):119–131. doi:10.1109/JSAC.2019.2951964.
   Web of Science ®Google Scholar
• Elsisi, M., M.-Q. Tran, K. Mahmoud, D.-E. A. Mansour, M. Lehtonen, and M. M. F. Darwish. 2022. “Effective IoT-Based Deep Learning Platform for Online Fault Diagnosis of Power Transformers Against Cyberattacks and Data Uncertainties.” Measurement 190:110686. doi:10.1016/j.measurement.2021.110686.
   Web of Science ®Google Scholar
• Gao, L. Y., H. Zhou, and S. P. Huang. 2005. “Intelligent Analysis of Root Cause Alarms in SDH Networks.” Power System Technology 2005 (6): 60–63. doi:10.3969/j.issn.1002-8684.2005.06.014.
   Google Scholar
• He, Q. S., W. T. Zhou, H. F. Xu, L. M. Cui, and Z. Y. Yu. 2017. “Network Fault Location Algorithm Based on Multi-Domain Partitioning in Communication and Information Networks.” Journal of Nanjing University of Posts and Telecommunications Natural Science 37 (1): 79–84. doi:10.14132/j.cnki.1673-5439.2017.01.012.
   Google Scholar
• Jiang, Q. C., B. Y. Huang, S. X. Zhang Ding, and X. F. Yan. 2016. “Bayesian Fault Diagnosis with Asynchronous Measurements and Its Application in Networked Distributed Monitoring.” IEEE Transactions on Industrial Electronics 63 (10): 6316–6324. doi:10.1109/TIE.2016.2577545.
   Web of Science ®Google Scholar
• Li, Q., Z. H. Cao, M. Tanveer, H. M. Pandey, and C. Wang. 2020. “An Effective Reliability Evaluation Method for Power Communication Network Based on Community Structure.” IEEE Transactions on Industrial Electronics 56 (4): 4489–4500. doi:10.1109/TIA.2019.2954272.
   Google Scholar
• Liu, D. C., X. P. Ji, B. Wang, and F. Tang. 2015. “Topological Vulnerability Analysis and Counter Measures of Electrical Communication Network Based on Complex Network Theory.” Power System Technology 39 (12): 3615–3621. doi:10.13335/j.1000-3673.pst.2015.12.042.
   Google Scholar
• Li, P. Z., Z. F. Xiao, Y. F. Wu, H. T. Liu, and Y. Wang. 2021. “Research on Complex Characteristics and Vulnerability of Power Communication Network.” In 2021 2nd International Conference on Computing, Networks and Internet of Things, Beijing, China, May 20–22, 2021, 1–6. Faisalabad: ACM. doi: 10.1145/3468691.3468723.
   Google Scholar
• Long, J. Y., R. X. Zhang, Z. Yang, Y. W. Huang, Y. Liu, and C. Li. 2022. “Self-Adaptation Graph Attention Network via Meta-Learning for Machinery Fault Diagnosis with Few Labeled Data.” IEEE Transactions on Instrumentation and Measurement 71:1–11. doi:10.1109/TIM.2022.3181894.
   Web of Science ®Google Scholar
• Ma, D. Y., Y. C. Liang, X. S. Zhao, R. C. Guan, and X. H. Shi. 2013. “Multi-BP Expert System for Fault Diagnosis of Power System.” Engineering Applications of Artificial Intelligence 26 (3): 937–944. doi:10.1016/j.engappai.2012.03.017.
   Web of Science ®Google Scholar
• Mohapatra, S., and P. M. Khilar. 2017. “Artificial Immune System Based Fault Diagnosis in Large Wireless Sensor Network Topology.” In 2017 IEEE Region 10 Conference (TENCON), Penang, Malaysia, November 5–8, 2017, 2687–2692. DC: IEEE. doi: 10.1109/TENCON.2017.8228317.
   Google Scholar
• Mohapatra, S., and P. M. Khilar. 2020. “Fault Diagnosis in Wireless Sensor Network Using Negative Selection Algorithm and Support Vector Machine.” Computational Intelligence 36 (3): 1374–1393. doi:10.1111/coin.12380.
   Web of Science ®Google Scholar
• Ozay, M., I. Esnaola, F. T. Y. Vural, S. R. Kulkarni, and H. V. Poor. 2016. “Machine Learning Methods for Attack Detection in the Smart Grid.” IEEE Transactions on Neural Networks and Learning Systems 27 (8): 1773–1786. doi:10.1109/TNNLS.2015.2404803.
   PubMed Web of Science ®Google Scholar
• Panda, M., and P. M. Khilar. 2015. “Distributed Self Fault Diagnosis Algorithm for Large Scale Wireless Sensor Networks Using Modified Three Sigma Edit Test.” Ad Hoc Networks 25: 170–184. doi:10.1016/j.adhoc.2014.10.006.
   Web of Science ®Google Scholar
• Rashid, M. M., M. Amar, I. Gondal, and J. Kamruzzaman. 2016. “A Data Mining Approach for Machine Fault Diagnosis Based on Associated Frequency Patterns.” Applied Intelligence 45 (3): 638–651. doi:10.1007/s10489-016-0781-3.
   Web of Science ®Google Scholar
• Song, J. K., L. M. Lin, Y. Z. Huang, and S.-Y. Hsieh. 2023. “Intermittent Fault Diagnosis of Split-Star Networks and Its Applications.” IEEE Transactions on Parallel and Distributed Systems 34 (4): 1253–1264. doi:10.1109/TPDS.2023.3242089.
   Web of Science ®Google Scholar
• Swaina, R. R., P. M. Khilar, and S. K. Bhoi. 2018. “Heterogeneous Fault Diagnosis for Wireless Sensor Networks.” Ad Hoc Networks 69: 15–37. doi:10.1016/j.adhoc.2017.10.012.
   Web of Science ®Google Scholar
• Wang, S. P., and D. M. Zhao. 2017. “Research Review and Prospects for Power Grid Fault Diagnosis.” Automation of Electric Power Systems 41 (19): 164–175. doi:10.7500/AEPS20170126001.
   Google Scholar
• Xiang, D. J., B. Wang, W. X. Guo, X. X. Chu, and Z. W. Yu. 2017. “Artificial Neural Network-Based Electricity Power System Refinement Safety Operation Rules.” Power System Protective Relaying and Control 45 (18): 32–37. doi:10.7667/PSPC161481.
   Google Scholar
• Zeng, Y., W. H. Zhu, B. R. Deng, and Y. Luo. 2016. “Crucial Node Decision Algorithm Based on Power Network Impact Factor in Electric Power Communication Network.” Power System Protection and Control 44 (2): 102–108. doi:10.7667/j.issn.1674-3415.2016.02.014.
   Google Scholar
• Zhang, X., J. Wei, S. Yue, and X. B. Zha. 2018. “An Analytical Method for Mapping Alarm Information to Model of Power Grid Fault Diagnosis.” IEEE Transactions on Electrical and Electronic Engineering 13 (6): 823–830. doi:10.1002/tee.22635.
   Web of Science ®Google Scholar
• Zhang, J., T. Zhang, Z. L. Yang, X. S. Zhu, and B. X. Yang. 2016. “Deep Convolutional Neural Network for Car Model Recognition Method.” Transducer and Microsystem Technologies 35 (11): 19–22. doi:10.13873/J.1000-9787(2016)11-0019-04.
   Google Scholar
• Zhao, X. L., and M. P. Jia. 2020. “A Novel Unsupervised Deep Learning Network for Intelligent Fault Diagnosis of Rotating Machinery.” Structural Health Monitoring 19 (6): 1745–1763. doi:10.1177/1475921719897317.
   Web of Science ®Google Scholar
• Zhong, Z. W., T. H. Xu, F. Wang, and T. Tang. 2018. “Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing.” Mathematical Problems in Engineering 8: 1–10. doi:10.1155/2018/9464971.
   Google Scholar