Exploration on Wireless Network-Based Monitoring and Early Warning Algorithms for Power Grounding Faults.
Published In: International Journal of High Speed Electronics & Systems, 2025, v. 34, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Hu, Yundong; Chen, Jintao 3 of 3
Abstract
Electricity is an indispensable resource in daily life. While it brings great convenience to people, it also brings some safety hazards, especially in the event of power failure, which may cause significant harm. Therefore, monitoring and warning of power faults are very important. Traditional power grounding fault monitoring and warning systems have problems such as untimely monitoring, inaccurate warning, and high error rates in fault location. In order to solve the above problems, this paper uses wireless networks to construct a power grounding fault monitoring and early warning system. The wireless network collected fault data based on the complexity of power grounding fault data, and used threshold monitoring method to analyze the collected data. The wireless network was used to construct a prediction model to monitor grounding faults. Through experiments, it can be found that the accuracy of the wireless network-based power fault monitoring system for predicting grounding faults was over 92.57%, and the average warning accuracy of 20 experiments was 93.856%. This paper studied a wireless network-based algorithm for monitoring and warning of power grounding faults, which can effectively improve the monitoring and warning capabilities of power systems, and reduce the risk of power equipment faults and the probability of power accidents. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of High Speed Electronics & Systems. 2025/06, Vol. 34, Issue 2, p1
- Document Type:Article
- Subject Area:Power and Energy
- Publication Date:2025
- ISSN:0129-1564
- DOI:10.1142/S0129156424400081
- Accession Number:184999721
- Copyright Statement:Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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