Fast online identification of power system dynamic behavior

Panagiotis N. Papadopoulos; Jovica V. Milanovic; Pratyasa Bhui; Nilanjan Senroy

doi:10.1109/PESGM.2017.8273882

Fast online identification of power system dynamic behavior

Panagiotis N. Papadopoulos, Jovica V. Milanovic, Pratyasa Bhui, Nilanjan Senroy

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

Abstract

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

Original language	English
Title of host publication	2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Print)	978-1-5386-2212-4
DOIs	https://doi.org/10.1109/PESGM.2017.8273882
Publication status	Published - 1 Feb 2018
Event	2017 IEEE Power and Energy Society General Meeting, PESGM 2017 - Chicago, United States Duration: 16 Jul 2017 → 20 Jul 2017

Conference

Conference	2017 IEEE Power and Energy Society General Meeting, PESGM 2017
Country/Territory	United States
City	Chicago
Period	16/07/17 → 20/07/17

Keywords

decision trees
dynamic security assessment
online transient stability

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10.1109/PESGM.2017.8273882

Cite this

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title = "Fast online identification of power system dynamic behavior",

abstract = "This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.",

keywords = "decision trees, dynamic security assessment, online transient stability",

author = "Papadopoulos, {Panagiotis N.} and Milanovic, {Jovica V.} and Pratyasa Bhui and Nilanjan Senroy",

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doi = "10.1109/PESGM.2017.8273882",

language = "English",

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TY - GEN

T1 - Fast online identification of power system dynamic behavior

AU - Papadopoulos, Panagiotis N.

AU - Milanovic, Jovica V.

AU - Bhui, Pratyasa

AU - Senroy, Nilanjan

PY - 2018/2/1

Y1 - 2018/2/1

N2 - This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

AB - This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

KW - decision trees

KW - dynamic security assessment

KW - online transient stability

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U2 - 10.1109/PESGM.2017.8273882

DO - 10.1109/PESGM.2017.8273882

M3 - Conference contribution book

AN - SCOPUS:85046349156

SN - 978-1-5386-2212-4

SP - 1

EP - 5

BT - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017

PB - IEEE

CY - Piscataway, N.J.

T2 - 2017 IEEE Power and Energy Society General Meeting, PESGM 2017

Y2 - 16 July 2017 through 20 July 2017

ER -

Fast online identification of power system dynamic behavior

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Keywords

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