The application study of deep learning technology in intelligent power supervision systems

Wang Ye, Jiesheng Yuan, Huayi Ye, Yufeng Chen, Ling Xie and Dailing Cai

International Journal of Low-Carbon Technologies, 2025, vol. 20, 112-118

Abstract: This study explores the application of a combined model incorporating bidirectional long short-term memory networks and gated recurrent units within intelligent power supervision systems, demonstrating its efficacy in recognizing anomalies within power grids. Based on historical operational data of two critical indicators—grid sectional security margin and generation-consumption balance margin—this paper constructs an anomaly detection model. This model effectively processes time series data, promptly identifying equipment malfunctions and abnormal load fluctuations, thereby enhancing the safety and stability of power grids.

Keywords: deep learning model; electric power system; anomaly recognition; early warning mechanism (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctae273 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:112-118.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().