用于城市电网故障检测和分类的自监督和自适应阈值增强型半监督学习方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-05-16 DOI:10.1016/j.egyai.2024.100377
Jiahao Zhang , Lan Cheng , Zhile Yang , Qinge Xiao , Sohail Khan , Rui Liang , Xinyu Wu , Yuanjun Guo
{"title":"用于城市电网故障检测和分类的自监督和自适应阈值增强型半监督学习方法","authors":"Jiahao Zhang ,&nbsp;Lan Cheng ,&nbsp;Zhile Yang ,&nbsp;Qinge Xiao ,&nbsp;Sohail Khan ,&nbsp;Rui Liang ,&nbsp;Xinyu Wu ,&nbsp;Yuanjun Guo","doi":"10.1016/j.egyai.2024.100377","DOIUrl":null,"url":null,"abstract":"<div><p>With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000430/pdfft?md5=321bcfbc9d04fdfdcd64a79a898c0c5c&pid=1-s2.0-S2666546824000430-main.pdf","citationCount":"0","resultStr":"{\"title\":\"An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids\",\"authors\":\"Jiahao Zhang ,&nbsp;Lan Cheng ,&nbsp;Zhile Yang ,&nbsp;Qinge Xiao ,&nbsp;Sohail Khan ,&nbsp;Rui Liang ,&nbsp;Xinyu Wu ,&nbsp;Yuanjun Guo\",\"doi\":\"10.1016/j.egyai.2024.100377\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.</p></div>\",\"PeriodicalId\":34138,\"journal\":{\"name\":\"Energy and AI\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000430/pdfft?md5=321bcfbc9d04fdfdcd64a79a898c0c5c&pid=1-s2.0-S2666546824000430-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000430\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824000430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

随着城市电网的快速发展和可再生能源的大规模集成,传统的电网故障诊断技术难以解决错综复杂的电网系统中故障诊断的复杂性。虽然人工智能技术为电网故障诊断提供了新的解决方案,但获取标注电网数据的困难限制了人工智能技术在这一领域的发展。为了应对这些挑战,本研究提出了一种带有自监督和自适应阈值(SAT-SSL)的半监督学习框架,用于电网故障检测和分类。与其他方法相比,我们的方法减少了对标记数据的依赖,同时保持了较高的识别准确率。首先,我们利用电网数据的频域分析来过滤异常事件,然后根据视觉特征对这些事件进行分类和标记,从而创建一个电网数据集。随后,我们采用 Yule-Walker 算法从电网数据中提取特征。然后,我们构建了一个半监督学习框架,结合自监督损失和动态阈值来增强信息提取能力和模型在不同场景下的适应性。最后,我们使用电网数据集和两个基准数据集来验证模型的功能。结果表明,我们的模型在不同场景和不同标签量下都能实现较低的错误率。在电网数据集中,当仅保留 5%的标签时,错误率仅为 6.15%,这证明该方法可以在有限的标签数据量下实现准确的电网故障检测和分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids

With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.

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
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions Supporting energy policy research with large language models: A case study in wind energy siting ordinances
×
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