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Title: Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation

Abstract

This paper addresses how to extend the concept of small-signal stability region (SSSR) to that of robust small-signal stability region (RSSSR), within which the system can remain stable even under perturbations caused by uncertain and volatile nodal injections, such as renewable generation. We first employ the structured perturbation theory to formulate the perturbations of nodal injections in state space. Both the intensity and the locations of perturbations can be taken into account. Then, we leverage the stability radius theory and structured singular value theory to define the RSSSR in parameter subspace, enabling a systematic analysis of small-signal stability of power systems under perturbations in a region-wise manner. The hyperplane-approximation method can be employed to construct a linear closed-form approximation of RSSSR boundaries. Case studies on the modified two-area system and New England 39-node system illustrate the new concept of RSSSR and its potential applications.

Authors:
ORCiD logo; ORCiD logo; ORCiD logo; ORCiD logo; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Natural Science Foundation of China (NNSFC)
OSTI Identifier:
1464637
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 2; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
power system stability analysis; robust stability region; small-signal stability; wind power generation

Citation Formats

Pan, Yanfei, Liu, Feng, Chen, Laijun, Wang, Jianhui, Qiu, Feng, Shen, Chen, and Mei, Shengwei. Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation. United States: N. p., 2018. Web. doi:10.1109/tpwrs.2017.2714759.
Pan, Yanfei, Liu, Feng, Chen, Laijun, Wang, Jianhui, Qiu, Feng, Shen, Chen, & Mei, Shengwei. Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation. United States. doi:10.1109/tpwrs.2017.2714759.
Pan, Yanfei, Liu, Feng, Chen, Laijun, Wang, Jianhui, Qiu, Feng, Shen, Chen, and Mei, Shengwei. Thu . "Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation". United States. doi:10.1109/tpwrs.2017.2714759.
@article{osti_1464637,
title = {Towards the Robust Small-Signal Stability Region of Power Systems Under Perturbations Such as Uncertain and Volatile Wind Generation},
author = {Pan, Yanfei and Liu, Feng and Chen, Laijun and Wang, Jianhui and Qiu, Feng and Shen, Chen and Mei, Shengwei},
abstractNote = {This paper addresses how to extend the concept of small-signal stability region (SSSR) to that of robust small-signal stability region (RSSSR), within which the system can remain stable even under perturbations caused by uncertain and volatile nodal injections, such as renewable generation. We first employ the structured perturbation theory to formulate the perturbations of nodal injections in state space. Both the intensity and the locations of perturbations can be taken into account. Then, we leverage the stability radius theory and structured singular value theory to define the RSSSR in parameter subspace, enabling a systematic analysis of small-signal stability of power systems under perturbations in a region-wise manner. The hyperplane-approximation method can be employed to construct a linear closed-form approximation of RSSSR boundaries. Case studies on the modified two-area system and New England 39-node system illustrate the new concept of RSSSR and its potential applications.},
doi = {10.1109/tpwrs.2017.2714759},
journal = {IEEE Transactions on Power Systems},
issn = {0885-8950},
number = 2,
volume = 33,
place = {United States},
year = {2018},
month = {3}
}