Analysis and Design of Power System Transformer Standard Based on Knowledge Graph

Authors

  • Yuzhong Zhou Electric Power Research Institute of China Southern Power Grid, China
  • Zhengping Lin Electric Power Research Institute of China Southern Power Grid, China
  • Yuan La China Southern Power Grid (China) image/svg+xml
  • Junkai Huang Electric Power Research Institute of Guizhou Power Grid Co. , Ltd., China
  • Xin Wang Electric Power Research Institute of China Southern Power Grid, China

DOI:

https://doi.org/10.4108/eetsis.v9i6.2642

Keywords:

Power System, knowledge graph, analysis and design

Abstract

The transformer can convert one kind of electric energy such as AC current and AC voltage into another kind of electric energy with the same frequency. Knowledge graph (KG) can describe various entities and concepts in the real world and their relationships, and it can be considered as a semantic network for power system transformer. Hence, it is of vital importance to analyze and design the power system transformer standard based on the knowledge graph. To this end, we firstly examine the power system transformer with one KG node and one eavesdropper E, where the eavesdropper E can overhear the network from the source, which may cause physical-layer secure issue and an outage probability event. To deal with the issue, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. We further investigate the power system transformer with multiple KG nodes which can help strengthen the system security and reliability. For such a system, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. Finally, we give some simulations to analyze the impact of secure transformer standard on the power system, and verify the accuracy of our proposed analytical expression for the the power system transformer standard based on the knowledge graph.

References

N. Dahlin and R. Jain, “Scheduling flexible nonpreemp-tive loads in smart-grid networks,” IEEE Trans. Control. Netw. Syst., vol. 9, no. 1, pp. 14–24, 2022.

E. Z. Serper and A. Altin-Kayhan, “Coverage and connectivity based lifetime maximization with topology update for WSN in smart grid applications,” Comput. Networks, vol. 209, p. 108940, 2022.

Z. Alavikia and M. Shabro, “A comprehensive layered approach for implementing internet of things-enabled smart grid: A survey,” Digit. Commun. Networks, vol. 8, no. 3, pp. 388–410, 2022.

S. Mishra, “Blockchain-based security in smart grid network,” Int. J. Commun. Networks Distributed Syst., vol. 28, no. 4, pp. 365–388, 2022.

H. Wang and Z. Huang, “Guest editorial: WWWJ special issue of the 21th international conference on web information systems engineering (WISE 2020),” World Wide Web, vol. 25, no. 1, pp. 305–308, 2022.

H. Wang, J. Cao, and Y. Zhang, Access Control Management in Cloud Environments. Springer, 2020. [Online]. Available: https://doi.org/10.1007/

-3-030-31729-4

H. Wang, Y. Wang, T. Taleb, and X. Jiang, “Editorial: Special issue on security and privacy in network computing,” World Wide Web, vol. 23, no. 2, pp. 951–957, 2020.

S. Tang, “Dilated convolution based CSI feedback compression for massive MIMO systems,” IEEE Trans. Vehic. Tech., vol. 71, no. 5, pp. 211–216, 2022.

X. Hu, C. Zhong, Y. Zhu, X. Chen, and Z. Zhang, “Programmable metasurface-based multicast systems: Design and analysis,” IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1763–1776, 2020.

S. Tang and L. Chen, “Computational intelligence and deep learning for next-generation edge-enabled industrial IoT,” IEEE Trans. Netw. Sci. Eng., vol. 9, no. 3, pp. 105–117, 2022.

X. Hu, C. Zhong, Y. Zhang, X. Chen, and Z. Zhang, “Location information aided multiple intelligent reflect-ing surface systems,” IEEE Trans. Commun., vol. 68, no. 12, pp. 7948–7962, 2020.

X. Lai, “Outdated access point selection for mobile edge computing with cochannel interference,” IEEE Trans. Vehic. Tech., vol. 71, no. 7, pp. 7445–7455, 2022.

D. Cai, P. Fan, Q. Zou, Y. Xu, Z. Ding, and Z. Liu, “Active device detection and performance analysis of massive non-orthogonal transmissions in cellular internet of things,” Science China information sciences, vol. 5, no. 8, pp. 182 301:1–182 301:18, 2022.

J. Lu and J. Xia, “Performance analysis for IRS-assisted MEC networks with unit selection,” Physical Communication, vol. 2022, no. 8.

K. He and Y. Deng, “Efficient memory-bounded optimal detection for GSM-MIMO systems,” IEEE Trans. Commun., vol. 70, no. 7, pp. 4359–4372, 2022.

R. Zhao and M. Tang, “Profit maximization in cache-aided intelligent computing networks,” Physical Commu-nication, vol. PP, no. 99, pp. 1–10, 2022.

L. Chen, “Physical-layer security on mobile edge computing for emerging cyber physical systems,” Computer Communications, vol. PP, no. 99, pp. 1–12, 2022.

S. Tang and X. Lei, “Collaborative cache-aided relaying networks: Performance evaluation and system optimiza-tion,” IEEE Journal on Selected Areas in Communications, vol. PP, no. 99, pp. 1–12, 2022.

R. Zhao and M. Tang, “Impact of direct links on intelligent reflect surface-aided MEC networks,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.

L. Zhang and C. Gao, “Deep reinforcement learning based IRS-assisted mobile edge computing under physical-layer security,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.

L. Chen and X. Lei, “Relay-assisted federated edge learn ing: Performance analysis and system optimization,” IEEE Transactions on Communications, vol. PP, no. 99, pp. 1–12, 2022.

J. Sun, X. Wang, Y. Fang, X. Tian, M. Zhu, J. Ou, and C. Fan, “Security performance analysis of relay networks based on-shadowed channels with rhis and cees,” Wireless Communications and Mobile Computing, vol. 2022, 2022.

X. Deng, S. Zeng, L. Chang, Y. Wang, X. Wu, J. Liang, J. Ou, and C. Fan, “An ant colony optimization-based routing algorithm for load balancing in leo satellite networks,” Wireless Communications and Mobile Computing, vol. 2022, 2022.

J. Lu and M. Tang, “Performance analysis for IRS-assisted MEC networks with unit selection,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.

Y. Wu and C. Gao, “Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach,” Physical Communication, vol. PP, no. 99, pp. 1–10, 2022.

C. Wang, W. Yu, F. Zhu, J. Ou, C. Fan, J. Ou, and D. Fan, “Uav-aided multiuser mobile edge computing networks with energy harvesting,” Wireless Communications and Mobile Computing, vol. 2022, 2022.

J. Chen, Y. Wang, J. Ou, C. Fan, X. Lu, C. Liao, X. Huang, and H. Zhang, “Albrl: Automatic load-balancing architecture based on reinforcement learning in software-defined networking,” Wireless Communica-tions and Mobile Computing, vol. 2022, 2022.

C. Ge, Y. Rao, J. Ou, C. Fan, J. Ou, and D. Fan, “Joint offloading design and bandwidth allocation for ris-aided multiuser mec networks,” Physical Communication, p. 101752, 2022.

C. Yang, B. Song, Y. Ding, J. Ou, and C. Fan, “Efficient data integrity auditing supporting provable data update for secure cloud storage,” Wireless Communications and Mobile Computing, vol. 2022, 2022.

J. Liu, Y. Zhang, J. Wang, T. Cui, L. Zhang, C. Li, K. Chen, S. Li, S. Feng, D. Xie et al., “Outage probability analysis for uav-aided mobile edge computing networks,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 9, no. 31, pp. e4–e4, 2022.

J. Liu, Y. Zhang, J. Wang, T. Cui, L. Zhang, C. Li, K. Chen, H. Huang, X. Zhou, W. Zhou et al., “The intelligent bi-directional relaying communication for edge intelligence based industrial iot networks: Intelligent bi-directional relaying communication,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 9, no. 32, pp. e4–e4, 2022.

Y. Tang and S. Lai, “Intelligent distributed data storage for wireless communications in b5g networks,” EAI Endorsed Transactions on Mobile Communications and Applications, vol. 2022, no. 8, pp. 121–128, 2022.

——, “Energy-efficient and high-spectrum-efficiency wireless transmission,” EAI Endorsed Transactions on Mobile Communications and Applications, vol. 2022, no. 8, pp. 129–135, 2022.

J. Liu and W. Zhou, “Deep model training and deployment on scalable iot networks: A survery,” EAI Endorsed Transactions on Scalable Information Systems, vol. 2022, no. 2, pp. 29–35, 2022.

Downloads

Published

12-10-2022

How to Cite

1.
Zhou Y, Lin Z, La Y, Huang J, Wang X. Analysis and Design of Power System Transformer Standard Based on Knowledge Graph. EAI Endorsed Scal Inf Syst [Internet]. 2022 Oct. 12 [cited 2024 Dec. 22];10(2):e6. Available from: https://publications.eai.eu/index.php/sis/article/view/2642

Most read articles by the same author(s)