Probabilistic robust small-signal stability framework using gaussian process learning

While most power system small-signal stability assessments rely on the reduced Jacobian, which depends non-linearly on the states, uncertain operating points introduce nontrivial hurdles in certifying the systems stability. In this paper, a novel probabilistic robust small-signal stability (PRS) fra...

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Main Authors: Pareek, Parikshit, Nguyen, Hung D.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/150724
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1507242021-06-09T01:24:56Z Probabilistic robust small-signal stability framework using gaussian process learning Pareek, Parikshit Nguyen, Hung D. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Probabilistic Robust Small-Signal Stability (PRS) Gaussian Process (GP) Learning While most power system small-signal stability assessments rely on the reduced Jacobian, which depends non-linearly on the states, uncertain operating points introduce nontrivial hurdles in certifying the systems stability. In this paper, a novel probabilistic robust small-signal stability (PRS) framework is developed for the power system based on Gaussian process (GP) learning. The proposed PRS assessment provides a robust stability certificate for a state subspace, such as that specified by the error bounds of the state estimation, with a given probability. With such a PRS certificate, all inner points of the concerned subspace will be stable with at least the corresponding probability. To this end, behavior of the critical eigenvalue of the reduced Jacobian with state points in a state subspace is learned using GP. The proposed PRS certificate along with the Subspace-based Search and Confidence-based Search mechanisms constitute a holistic framework catering to all scenarios. The proposed framework is a powerful approach to assess the stability under uncertainty because it does not require input uncertainty distributions and other state-specific input-to-output approximations. Further, the critical eigenvalue behavior in a state subspace is analyzed using an upper bound of the eigenvalue variations and their inferences are discussed in detail. The results on three-machine nine-bus WSCC system show that the proposed certificate can find the robust stable state subspace with a given probability. Accepted version 2021-06-09T01:24:56Z 2021-06-09T01:24:56Z 2020 Journal Article Pareek, P. & Nguyen, H. D. (2020). Probabilistic robust small-signal stability framework using gaussian process learning. Electric Power Systems Research, 188, 106545-. https://dx.doi.org/10.1016/j.epsr.2020.106545 0378-7796 0000-0003-4688-2021 0000-0003-2610-5161 https://hdl.handle.net/10356/150724 10.1016/j.epsr.2020.106545 2-s2.0-85088374444 188 106545 en Electric Power Systems Research © 2020 Elsevier B.V. All rights reserved. This paper was published in Electric Power Systems Research and is made available with permission of Elsevier B.V. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Probabilistic Robust Small-Signal Stability (PRS)
Gaussian Process (GP) Learning
spellingShingle Engineering::Electrical and electronic engineering
Probabilistic Robust Small-Signal Stability (PRS)
Gaussian Process (GP) Learning
Pareek, Parikshit
Nguyen, Hung D.
Probabilistic robust small-signal stability framework using gaussian process learning
description While most power system small-signal stability assessments rely on the reduced Jacobian, which depends non-linearly on the states, uncertain operating points introduce nontrivial hurdles in certifying the systems stability. In this paper, a novel probabilistic robust small-signal stability (PRS) framework is developed for the power system based on Gaussian process (GP) learning. The proposed PRS assessment provides a robust stability certificate for a state subspace, such as that specified by the error bounds of the state estimation, with a given probability. With such a PRS certificate, all inner points of the concerned subspace will be stable with at least the corresponding probability. To this end, behavior of the critical eigenvalue of the reduced Jacobian with state points in a state subspace is learned using GP. The proposed PRS certificate along with the Subspace-based Search and Confidence-based Search mechanisms constitute a holistic framework catering to all scenarios. The proposed framework is a powerful approach to assess the stability under uncertainty because it does not require input uncertainty distributions and other state-specific input-to-output approximations. Further, the critical eigenvalue behavior in a state subspace is analyzed using an upper bound of the eigenvalue variations and their inferences are discussed in detail. The results on three-machine nine-bus WSCC system show that the proposed certificate can find the robust stable state subspace with a given probability.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Pareek, Parikshit
Nguyen, Hung D.
format Article
author Pareek, Parikshit
Nguyen, Hung D.
author_sort Pareek, Parikshit
title Probabilistic robust small-signal stability framework using gaussian process learning
title_short Probabilistic robust small-signal stability framework using gaussian process learning
title_full Probabilistic robust small-signal stability framework using gaussian process learning
title_fullStr Probabilistic robust small-signal stability framework using gaussian process learning
title_full_unstemmed Probabilistic robust small-signal stability framework using gaussian process learning
title_sort probabilistic robust small-signal stability framework using gaussian process learning
publishDate 2021
url https://hdl.handle.net/10356/150724
_version_ 1702431250532270080