Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems

Yuzhong Zhou; Zhengping Lin; Liang Tu; Yufei Song; Zhengrong Wu

doi:10.4108/eetinis.v9i32.1268

Authors

Yuzhong Zhou Electric Power Research Institute of China Southern Power Grid, Guangzhou, Guangdong, China
Zhengping Lin Electric Power Research Institute of China Southern Power Grid, Guangzhou, Guangdong, China
Liang Tu Electric Power Research Institute of China Southern Power Grid, Guangzhou, Guangdong, China
Yufei Song China Southern Power Grid, Guangzhou, Guangdong, China
Zhengrong Wu China Southern Power Grid, Guangzhou, Guangdong, China

DOI:

https://doi.org/10.4108/eetinis.v9i32.1268

Keywords:

Big data, knowledge graph, fault detection, electric power systems

Abstract

Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection for electric power systems. In particular, this paper analyzes a distributed knowledge graph framework for fault detection in the electric power systems, where multiple devices train their local detection models used for fault detection assisted with a central server. Each device owns its local data set composed of historical fault information and current device state, which can be used to train a local model for fault detection. To enhance the detection performance, the distributed devices interact with each other in the KG framework, where the devices ought to achieve the regional computation in addition to the model aggregation within a specified latency threshold. Through searching for the vibrant qualities together with determined ability at the devices, we enhance the knowledge graph framework by the optimum variety of energetic devices together with the restriction of latency as well as data transmission. Particularly, two data transmission bandwidth allocation (BA) schemes are developed for the distributed knowledge graph framework, through which scheme I is actually bared after the instantaneous device state information (DSI), and scheme II utilizes particle swarm optimization (PSO) technique along with the statistical DSI. The results of simulation on the examination as well as convergence are lastly demonstrated to show the advantages of the proposed distributed KG framework in the fault detection for the electric power systems.

Downloads

Download data is not yet available.

References

S. Tang and L. Chen, "Computational intelligence and deep learning for next-generation edge-enabled industrial IoT," IEEE Trans. Network Science and Engineering, vol. PP, no. 99, pp. 1–12, 2022. DOI: https://doi.org/10.1109/TNSE.2022.3180632

S. Tang, "Dilated convolution based CSI feedback compression for massive MIMO systems," IEEE Trans. Vehic. Tech., vol. PP, no. 99, pp. 1–5, 2021.

J. Xia, "Secure cache-aided multi-relay networks in the presence of multiple eavesdroppers," IEEE Trans. Commun., vol. 67, no. 11, pp. 7672–7685, 2019. DOI: https://doi.org/10.1109/TCOMM.2019.2935047

T. Q. Duong, D. V. Huynh, Y. Li, E. Garcia, and K. Sun, "Digital twin-enabled 6g aerial edge computing with ultra-reliable and low-latency communications," in Proc. 1st International Conference on 6G Networking, 2022, pp. Paris, France, 1–6.

Y. Li, D. V. Huynh, T. Do-Duy, E. Garcia-Palacios, and T. Q. Duong, "Unmanned aerial vehicle-aided edge networks with ultra-reliable low-latency communications: A digital twin approach," IET Sig. Proc., vol. 2022, pp. 1–12, 2022, DOI: 10.1049/sil2.12128. DOI: https://doi.org/10.1049/sil2.12128

C. Li, "Dynamic offloading formultiuserMuti-CAP MEC networks: A deep reinforcement learning approach," IEEE Trans. Vehic. Tech., vol. 70, no. 3, pp. 2922–2927, 2021. DOI: https://doi.org/10.1109/TVT.2021.3058995

Y. Guo, "Efficient and flexible management for industrial internet of things: A federated learning approach," Computer Networks, vol. 192, pp. 1–9, June 2021. DOI: https://doi.org/10.1016/j.comnet.2021.108122

S. Tang, "Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks," Phys. Commun., vol. 47, no. 101381, pp. 1–9, 2021. DOI: https://doi.org/10.1016/j.phycom.2021.101381

K. He, "Learning based signal detection for MIMO systems with unknown noise statistics," IEEE Trans. Commun., vol. 69, pp. 3025–3038, 2021. DOI: https://doi.org/10.1109/TCOMM.2021.3058999

X. Lai, "Secure mobile edge computing networks in the presence of multiple eavesdroppers," IEEE Trans. Commun., vol. PP, pp. 1–12, 2021. DOI: https://doi.org/10.1109/TCOMM.2021.3119075

S. Lai, "Intelligent secure mobile edge computing for beyond 5G wireless networks," Phys. Commun., vol. 45, no. 101283, pp. 1–8, 2021. DOI: https://doi.org/10.1016/j.phycom.2021.101283

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in AISTATS, vol. 54, 2017, pp. 1273–1282.

K. Yang, T. Jiang, Y. Shi, and Z. Ding, "Federated learning via over-the-air computation," IEEE Trans. Wirel. Commun., vol. 19, no. 3, pp. 2022–2035, 2020. DOI: https://doi.org/10.1109/TWC.2019.2961673

M. Abramowitz and I. A. Stegun, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, 10th ed. New York: Dover: Academic, 1972.