利用量子人工智能和群体抗扰度加强电力系统故障检测 电力系统中的量子人工智能故障检测与群体抗扰度优化

M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya
{"title":"利用量子人工智能和群体抗扰度加强电力系统故障检测 电力系统中的量子人工智能故障检测与群体抗扰度优化","authors":"M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya","doi":"10.1049/qtc2.12106","DOIUrl":null,"url":null,"abstract":"Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).","PeriodicalId":507937,"journal":{"name":"IET Quantum Communication","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems\",\"authors\":\"M. L. Sworna Kokila, V. Bibin Christopher, G. Ramya\",\"doi\":\"10.1049/qtc2.12106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).\",\"PeriodicalId\":507937,\"journal\":{\"name\":\"IET Quantum Communication\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Quantum Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1049/qtc2.12106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Quantum Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/qtc2.12106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

量子计算和深度学习最近在各行各业大受欢迎,有望带来革命性的进步。作者介绍了 QC-PCSANN-CHIO-FD,这是一种通过结合量子计算、深度学习和优化算法来增强电力系统故障检测的新方法。该网络以金字塔卷积洗牌注意神经网络(PCSANN)为基础,利用冠状病毒群免疫优化器进行了优化,显示出良好的效果。最初,历史数据集用于故障检测。预处理包括使用自适应变异贝叶斯滤波处理缺失数据和异常值,然后进行双域特征提取,以提取灰度统计特征。PCSANN 对这些特征进行处理,以检测故障。提出了冠状病毒群免疫优化算法来优化 PCSANN,以实现精确的故障检测。提出的 QC-PCSANN-CHIO-FD 方法的性能达到了 24.11%、28.56% 和 22.73% 的高特异性,21.89%、23.04% 和 9.51% 的低计算时间,25.289%、15.35% 和 19.91% 的高 ROC,以及 8.65%、13.8% 和 7.与现有方法相比,准确率提高了 15%,如基于量子计算的深度学习结合用于电力系统故障诊断(QC-ANN-FD)、利用混合量子经典深度学习的电力系统故障诊断(QC-CRBM-FD)、机器学习在电力系统故障识别中的应用等:近期发展和未来方向(QC-RF-FD)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems
Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Enhanced power system fault detection using quantum‐AI and herd immunity quantum‐AI fault detection with herd immunity optimisation in power systems Exploring the fusion of lattice‐based quantum key distribution for secure Internet of Things communications Quantum calculi and formalisms for system and network security: A bibliographic insights and synoptic review Quantum‐inspired Arecanut X‐ray image classification using transfer learning Quantum computing challenges and solutions in software industry—A multivocal literature review
×
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