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Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges

Liang Luo1, Xingmei Li1, Kaijiang Yang1, Mengyang Wei1, Jiong Chen1, Junqian Yang1, Liang Yao2,*
1 Dali Power Supply Bureau of Yunnan Power Grid Co., Ltd., Dali, 671000, China
2 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
* Corresponding Author: Liang Yao. Email:
(This article belongs to this Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)

Computer Modeling in Engineering & Sciences 2023, 134(3), 1565-1595. https://doi.org/10.32604/cmes.2022.021198

Received 31 December 2021; Accepted 03 May 2022; Issue published 20 September 2022

Abstract

The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and outline future directions in this area. In particular, we divide intelligent identification over power big data into data acquisition and storage processes, anomaly detection and fault discrimination processes, and fault tracing for dispatching operations during communication. A detailed survey of the solutions to the challenges in intelligent identification over power big data is then presented. Moreover, opportunities and future directions are outlined.

Keywords

Data acquisition; data storage; anomaly detection; service fault-tolerant scheduling

Cite This Article

Luo, L., Li, X., Yang, K., Wei, M., Chen, J. et al. (2023). Intelligent Identification over Power Big Data: Opportunities, Solutions, and Challenges. CMES-Computer Modeling in Engineering & Sciences, 134(3), 1565–1595.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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