Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network

Feifei Bai; Xiaoru Wang; Yilu Liu; Xinyu Liu; Yue Xiang; Yong Liu

doi:10.17775/CSEEJPES.2016.00030

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Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network

更新时间：2025-12-18

- Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network
- Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network
- 中国电机工程学会电力与能源系统学报(英文) 2016年2卷第3期页码：10-18
- 作者机构：
  
  1. Southwest Jiaotong University,,, CN,Sichuan,Chengdu
  2. University of Tennessee Knoxville,,, US,TN,Knoxville
  3. Sichuan University,,, CN,Sichuan,Chengdu
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00030
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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Feifei Bai, Xiaoru Wang, Yilu Liu, 等. Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):10-18.

Feifei Bai, Xiaoru Wang, Yilu Liu, et al. Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 10-18.
Feifei Bai, Xiaoru Wang, Yilu Liu, 等. Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network[J]. 中国电机工程学会电力与能源系统学报(英文), 2016,2(3):10-18. DOI： 10.17775/CSEEJPES.2016.00030.

Feifei Bai, Xiaoru Wang, Yilu Liu, et al. Measurement-based frequency dynamic response estimation using geometric template matching and recurrent artificial neural network[J]. CSEE Journal of Power and Energy Systems, 2016, 2(3): 10-18. DOI： 10.17775/CSEEJPES.2016.00030.

摘要

Abstract

Understanding power system dynamics after an event occurs is essential for the purpose of online stability assessment and control applications. Wide area measurement systems (WAMS) based on synchrophasors make power system dynamics visible to system operators

delivering an accurate picture of overall operating conditions. However

in actual field implementations

some measurements can be inaccessible for various reasons

e.g.

most notably communication failure. To reconstruct these inaccessible measurements

in this paper

the radial basis function artificial neural network (RBF-ANN) is used to estimate the system dynamics. In order to find the best input features of the RBF-ANN model

geometric template matching (GeTeM) and quality-threshold (QT) clustering are employed from the time series analysis to compute the similarity of frequency dynamic responses in different locations of the power system. The proposed method is tested and verified on the Eastern Interconnection (EI) transmission system in the United States. The results obtained indicate that the proposed approach provides a compact and efficient RBF-ANN model that accurately estimates the inaccessible frequency dynamic responses under different operating conditions and with fewer inputs.

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