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Title: Dynamic-Feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction

Abstract

In interconnected power systems, dynamic model reduction can be applied on generators outside the area of interest to mitigate the computational cost with transient stability studies. This paper presents an approach of deriving the reduced dynamic model of the external area based on dynamic response measurements, which comprises of three steps, dynamic-feature extraction, attribution and reconstruction (DEAR). In the DEAR approach, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal ‘basis’ of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original external system. Network model is un-changed in the DEAR method. Tests on several IEEE standard systems show that the proposed method gets better reduction ratio and response errors than the traditional coherency aggregation methods.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1159315
Report Number(s):
PNNL-SA-94038
KJ0401000
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Power Systems, 29(5):2049-2059
Country of Publication:
United States
Language:
English
Subject:
Dynamic response, model reduction, orthogonal decomposition, feature extraction, power systems.

Citation Formats

Wang, Shaobu, Lu, Shuai, Zhou, Ning, Lin, Guang, Elizondo, Marcelo A., and Pai, M. A. Dynamic-Feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction. United States: N. p., 2014. Web. doi:10.1109/TPWRS.2014.2301032.
Wang, Shaobu, Lu, Shuai, Zhou, Ning, Lin, Guang, Elizondo, Marcelo A., & Pai, M. A. Dynamic-Feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction. United States. doi:10.1109/TPWRS.2014.2301032.
Wang, Shaobu, Lu, Shuai, Zhou, Ning, Lin, Guang, Elizondo, Marcelo A., and Pai, M. A. Thu . "Dynamic-Feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction". United States. doi:10.1109/TPWRS.2014.2301032.
@article{osti_1159315,
title = {Dynamic-Feature Extraction, Attribution and Reconstruction (DEAR) Method for Power System Model Reduction},
author = {Wang, Shaobu and Lu, Shuai and Zhou, Ning and Lin, Guang and Elizondo, Marcelo A. and Pai, M. A.},
abstractNote = {In interconnected power systems, dynamic model reduction can be applied on generators outside the area of interest to mitigate the computational cost with transient stability studies. This paper presents an approach of deriving the reduced dynamic model of the external area based on dynamic response measurements, which comprises of three steps, dynamic-feature extraction, attribution and reconstruction (DEAR). In the DEAR approach, a feature extraction technique, such as singular value decomposition (SVD), is applied to the measured generator dynamics after a disturbance. Characteristic generators are then identified in the feature attribution step for matching the extracted dynamic features with the highest similarity, forming a suboptimal ‘basis’ of system dynamics. In the reconstruction step, generator state variables such as rotor angles and voltage magnitudes are approximated with a linear combination of the characteristic generators, resulting in a quasi-nonlinear reduced model of the original external system. Network model is un-changed in the DEAR method. Tests on several IEEE standard systems show that the proposed method gets better reduction ratio and response errors than the traditional coherency aggregation methods.},
doi = {10.1109/TPWRS.2014.2301032},
journal = {IEEE Transactions on Power Systems, 29(5):2049-2059},
number = ,
volume = ,
place = {United States},
year = {Thu Sep 04 00:00:00 EDT 2014},
month = {Thu Sep 04 00:00:00 EDT 2014}
}
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