Feature extraction and visualization of power system transient stability results

Sudipta Dutta; Thomas J Overbye

doi:10.1109/TPWRS.2013.2283276

Feature extraction and visualization of power system transient stability results

Sudipta Dutta, Thomas J Overbye

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16000-bus real-world system model.

Original language	English (US)
Article number	6621055
Pages (from-to)	966-973
Number of pages	8
Journal	IEEE Transactions on Power Systems
Volume	29
Issue number	2
DOIs	https://doi.org/10.1109/TPWRS.2013.2283276
State	Published - Mar 1 2014

Keywords

Clustering
feature extraction
model errors
power system visualization
spark lines
transient stability data

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

Access to Document

10.1109/TPWRS.2013.2283276

Fingerprint Dive into the research topics of 'Feature extraction and visualization of power system transient stability results'. Together they form a unique fingerprint.

Cite this

@article{14acb7eeb22a43fe8181c2b0f8e0bc2c,

title = "Feature extraction and visualization of power system transient stability results",

abstract = "Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16000-bus real-world system model.",

keywords = "Clustering, feature extraction, model errors, power system visualization, spark lines, transient stability data",

author = "Sudipta Dutta and Overbye, {Thomas J}",

year = "2014",

month = mar,

day = "1",

doi = "10.1109/TPWRS.2013.2283276",

language = "English (US)",

volume = "29",

pages = "966--973",

journal = "IEEE Transactions on Power Systems",

issn = "0885-8950",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "2",

}

TY - JOUR

T1 - Feature extraction and visualization of power system transient stability results

AU - Dutta, Sudipta

AU - Overbye, Thomas J

PY - 2014/3/1

Y1 - 2014/3/1

N2 - Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16000-bus real-world system model.

AB - Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16000-bus real-world system model.

KW - Clustering

KW - feature extraction

KW - model errors

KW - power system visualization

KW - spark lines

KW - transient stability data

UR - http://www.scopus.com/inward/record.url?scp=84897661281&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897661281&partnerID=8YFLogxK

U2 - 10.1109/TPWRS.2013.2283276

DO - 10.1109/TPWRS.2013.2283276

M3 - Article

AN - SCOPUS:84897661281

VL - 29

SP - 966

EP - 973

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 2

M1 - 6621055

ER -