数据驱动的风电场频率调节特性集总动态建模

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shaolin Li, Jianmou Lu, Shiyao Qin, Yang Hu, Fang Fang
{"title":"数据驱动的风电场频率调节特性集总动态建模","authors":"Shaolin Li,&nbsp;Jianmou Lu,&nbsp;Shiyao Qin,&nbsp;Yang Hu,&nbsp;Fang Fang","doi":"10.1049/cps2.12031","DOIUrl":null,"url":null,"abstract":"<p>High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment; therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non-linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data-based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non-linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions; then, the trained model is tested by the data of each working condition to verify the accuracy and universality.</p>","PeriodicalId":36881,"journal":{"name":"IET Cyber-Physical Systems: Theory and Applications","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12031","citationCount":"0","resultStr":"{\"title\":\"Data-driven lumped dynamic modelling of wind farm frequency regulation characteristics\",\"authors\":\"Shaolin Li,&nbsp;Jianmou Lu,&nbsp;Shiyao Qin,&nbsp;Yang Hu,&nbsp;Fang Fang\",\"doi\":\"10.1049/cps2.12031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment; therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non-linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data-based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non-linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions; then, the trained model is tested by the data of each working condition to verify the accuracy and universality.</p>\",\"PeriodicalId\":36881,\"journal\":{\"name\":\"IET Cyber-Physical Systems: Theory and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2022-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cps2.12031\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Cyber-Physical Systems: Theory and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Cyber-Physical Systems: Theory and Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cps2.12031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

风电在电网中的高比重导致电力系统频率失稳问题,这就要求风电场自身具备调频能力;因此,对风电场频率调节(WFFR)响应特性进行建模显得尤为重要。在建模过程中,一般需要针对每种工况分别建立模型,这将带来巨大的工作量。此外,当频率响应为非线性时,模型的精度降低。因此,本文对不同工况下WFFR响应特性的建模进行了研究。介绍了一种基于WFFR策略和建模方法的数据预处理方法。然后,构建了不同工况下WFFR响应特性的基于数据的传递函数模型。然后,利用间隙度量技术测量不同模型之间的间隙,分析模型之间的动态相似性。最后,为了弥补传递函数模型的缺陷,利用所有工况的集总数据,构建了WFFR响应特性的非线性自回归外生输入神经网络(NARXNN)模型;然后用各工况数据对训练好的模型进行检验,验证模型的准确性和通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Data-driven lumped dynamic modelling of wind farm frequency regulation characteristics

Data-driven lumped dynamic modelling of wind farm frequency regulation characteristics

High proportion of wind power in the power grid leads to the problem of power system frequency instability, which requires the wind farm itself to have the ability of frequency adjustment; therefore, it is particularly important to conduct modelling of wind farm frequency regulation (WFFR) response characteristics. During the modelling process, it is generally necessary to establish a model for each working condition separately, which will bring huge workload. In addition, the accuracy of the model decreases when the frequency response is non-linear. Therefore, this paper investigates the modelling of WFFR response characteristics in different working conditions. A data preprocessing method based on WFFR strategy and modelling methods is introduced. Then, data-based transfer function models of WFFR response characteristics for different working conditions are constructed. After that, the gaps between different models are measured using a gap metric technique to analyse dynamic similarity between models. Finally, in order to make up for the defect of transfer function models, a non-linear autoregressive with exogenous input neural networks (NARXNN) model of WFFR response characteristics is constructed utilising lumped data of all working conditions; then, the trained model is tested by the data of each working condition to verify the accuracy and universality.

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
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
×
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