Research on Knowledge Management of Novel Power System Based on Deep Learning

Zhengping Lin; Jiaxin Lin

doi:10.4108/eetsis.v9i6.2634

Authors

Zhengping Lin Electric Power Research Institute of China Southern Power Grid Company, China
Jiaxin Lin Guangdong Power Grid Co.,Ltd, China

DOI:

https://doi.org/10.4108/eetsis.v9i6.2634

Keywords:

Deep learning, knowledge management, power system

Abstract

With the rapid development of information technology, power system has been developed and applied rapidly. In the power system, fault detection is very important and is one of the key means to ensure the operation of power system. How to effectively improve the ability of fault detection is the most important issue in the research of power system. Traditional fault detection mainly relies on manual daily inspection, and power must be cut off during maintenance, which affects the normal operation of the power grid. In case of emergency, the equipment can not be powered off, which may lead to missed test and bury potential safety hazards. To solve these issues, in this paper, we study the knowledge management based power system by employing the deep learning technique. Specifically, we firstly introduce the data augmentation in the knowledge management based power system and the associated activated functions. We then develop the deep network architecture to extract the local spatial features among the data of the knowledge management based power system. We further provide several training strategies for the data classification in the knowledge management based power system, where the cross entropy based loss function is used. Finally, some experimental results are demonstrated to show the effectiveness of the proposed studies for the knowledge management based power system.

References

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/10.1007/978-3-030-31729-4

X. Hu, J. Wang, and C. Zhong, “Statistical CSI based design for intelligent reflecting surface assisted MISO systems,” Science China: Information Science, vol. 63, no. 12, p. 222303, 2020.

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.

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, 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.

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.

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 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. 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.

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

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.

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

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.

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.

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.

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. 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.

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.

J. Liu, Y. Zhang, J. Wang, T. Cui, L. Zhang, C. Li, K. Chen, S. Li, and S. Feng, “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.