Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement

Rajalakshmi D, Rajesh Kambattan K, Sudharson K, Suresh Kumar A, Vanitha R
{"title":"Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement","authors":"Rajalakshmi D, Rajesh Kambattan K, Sudharson K, Suresh Kumar A, Vanitha R","doi":"10.54392/irjmt2445","DOIUrl":null,"url":null,"abstract":"This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":" 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Research Journal of Multidisciplinary Technovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54392/irjmt2445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用光 GBM 提高无线输电中的实时故障检测效率
本研究介绍了 WirelessGridBoost,这是一个创新框架,旨在通过利用 LightGBM 机器学习算法的强大功能,彻底改变无线电网中的实时故障检测。由于电网运行的复杂性和通信基础设施的局限性,电网中的传统故障检测系统经常面临延迟和可扩展性等挑战。为了克服这些挑战,WirelessGridBoost 将高效梯度提升决策树算法 LightGBM 与无线技术相结合,实现了先进的故障检测能力。经过对历史传感器数据的训练,LightGBM 模型在辨别电网运行中固有的复杂故障模式方面表现出了非凡的能力。WirelessGridBoost 部署在电网中具有战略地位的无线节点上,能够实时迅速地识别异常情况。在实际电网测试平台上进行的大量模拟和实验验证了 WirelessGridBoost 的有效性,与传统方法相比,其故障检测准确率达到 96.80%,延迟时间缩短了 38%。这项研究通过创新的 WirelessGridBoost 框架,为提高无线电网的故障检测效率提供了一条大有可为的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
0.50
自引率
0.00%
发文量
0
期刊最新文献
Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement Quantum Chemical Computational Studies on the Structural Aspects, Spectroscopic Properties, Hirshfeld Surfaces, Donor-Acceptor Interactions and Molecular Docking of Clascosterone: A Promising Antitumor Agent Evaluation of Structural Stability of Four-Storied building using Non-Destructive Testing Techniques Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1