基于非侵入式负荷监测的微电网故障趋势预警研究

Qingguang Yu, Zhicheng Jiang, Yuming Liu, G. Long, M. Guo, Di Yang
{"title":"基于非侵入式负荷监测的微电网故障趋势预警研究","authors":"Qingguang Yu, Zhicheng Jiang, Yuming Liu, G. Long, M. Guo, Di Yang","doi":"10.1109/ICCSSE50399.2020.9171952","DOIUrl":null,"url":null,"abstract":"As the prospective study of non-intrusive load monitoring (NILM) in load decomposition, the fault detection and classification is promising. After describing the structure of offshore oil platform power system connected with offshore wind farm, this paper presented a framework, for using NILM for fault detection and electricity behavior in offshore oil platform microgrid. The data acquisition from smart power meter was adopted to train the designed algorithm and strategy with GPU, the moving average convergence divergence strategy and differential value prediction line for the early warning of failure with installation load tendency was approached to solve the problem: “Early stopping–But when?”","PeriodicalId":400708,"journal":{"name":"2020 IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Research of Early Warning of Failure with Load Tendency Based on Non-intrusive Load Monitoring in Microgrid\",\"authors\":\"Qingguang Yu, Zhicheng Jiang, Yuming Liu, G. Long, M. Guo, Di Yang\",\"doi\":\"10.1109/ICCSSE50399.2020.9171952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the prospective study of non-intrusive load monitoring (NILM) in load decomposition, the fault detection and classification is promising. After describing the structure of offshore oil platform power system connected with offshore wind farm, this paper presented a framework, for using NILM for fault detection and electricity behavior in offshore oil platform microgrid. The data acquisition from smart power meter was adopted to train the designed algorithm and strategy with GPU, the moving average convergence divergence strategy and differential value prediction line for the early warning of failure with installation load tendency was approached to solve the problem: “Early stopping–But when?”\",\"PeriodicalId\":400708,\"journal\":{\"name\":\"2020 IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE)\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSSE50399.2020.9171952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSSE50399.2020.9171952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

作为负荷分解中非侵入式负荷监测(NILM)的研究方向,故障检测与分类具有广阔的应用前景。在描述了与海上风电场连接的海上石油平台电力系统结构的基础上,提出了利用NILM进行海上石油平台微电网故障检测和电力行为分析的框架。采用智能电能表采集的数据,利用GPU对设计的算法和策略进行训练,探讨了基于安装负荷趋势的故障预警的移动平均收敛发散策略和差分值预测线,解决了“早停-但何时停?”
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Early Warning of Failure with Load Tendency Based on Non-intrusive Load Monitoring in Microgrid
As the prospective study of non-intrusive load monitoring (NILM) in load decomposition, the fault detection and classification is promising. After describing the structure of offshore oil platform power system connected with offshore wind farm, this paper presented a framework, for using NILM for fault detection and electricity behavior in offshore oil platform microgrid. The data acquisition from smart power meter was adopted to train the designed algorithm and strategy with GPU, the moving average convergence divergence strategy and differential value prediction line for the early warning of failure with installation load tendency was approached to solve the problem: “Early stopping–But when?”
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信