使用无监督特征学习技术的修正 ELM 集合对可再生集成电力系统进行静态安全评估

IF 4 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Mukesh Singh, Sushil Chauhan
{"title":"使用无监督特征学习技术的修正 ELM 集合对可再生集成电力系统进行静态安全评估","authors":"Mukesh Singh,&nbsp;Sushil Chauhan","doi":"10.1016/j.compeleceng.2024.109881","DOIUrl":null,"url":null,"abstract":"<div><div>The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109881"},"PeriodicalIF":4.0000,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique\",\"authors\":\"Mukesh Singh,&nbsp;Sushil Chauhan\",\"doi\":\"10.1016/j.compeleceng.2024.109881\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.</div></div>\",\"PeriodicalId\":50630,\"journal\":{\"name\":\"Computers & Electrical Engineering\",\"volume\":\"120 \",\"pages\":\"Article 109881\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Electrical Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0045790624008073\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790624008073","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

摘要

将可再生能源纳入电力系统给静态安全评估带来了各种挑战,包括可再生能源发电的间歇性和可变性、预测的不确定性以及对电网稳定性的影响。要克服这些挑战,需要利用先进的建模方法、改进预测算法、加强电网监测和控制系统,以及开发专为集成了可再生能源发电的电力系统而设计的稳健的静态安全评估方法。本研究提出了一种基于改进型极限学习机(ELM)的集合方法,将 ELM 与 Levenberg-Marquardt (LM)反向传播技术相结合,以提高预测的准确性和鲁棒性。此外,还通过自动编码器形式的无监督特征学习技术提高了计算效率,从而降低了维度诅咒。集合技术为评估电力系统在可再生能源带来的不确定性情况下的静态安全性提供了全面的解决方案。通过使用成熟的蒙特卡罗(MC)模拟方法模拟随机太阳能和风能情况,将不确定性纳入测试系统。通过对修改后的 IEEE 14 总线、30 总线、118 总线和印度实用 75 总线系统进行数值测试,证明了这种方法的有效性。结果表明,所提出的模型在可靠性和效率方面优于基础学习者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique
The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.
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
Computers & Electrical Engineering
Computers & Electrical Engineering 工程技术-工程:电子与电气
CiteScore
9.20
自引率
7.00%
发文量
661
审稿时长
47 days
期刊介绍: The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency. Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.
×
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