Wavelet-based event detection method using PMU data

Do In Kim; Tae Yoon Chun; Sung Hwa Yoon; Gyul Lee; Yong June Shin

doi:10.1109/TSG.2015.2478421

Wavelet-based event detection method using PMU data

Do In Kim, Tae Yoon Chun, Sung Hwa Yoon, Gyul Lee, Yong June Shin

Department of Electrical and Electronic Engineering

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

100 Citations (Scopus)

Abstract

In order to deal with the nonstationary signatures of phasor measurement units (PMU) signals, this paper presents a wavelet-based detection algorithm. Moreover, for an application to PMU for event detection purpose, it is necessary for us to classify detected events into unexpected real power related accidents, such as generator trip or automated control, such as reactive power injection. The proposed normalized wavelet energy function calculates the root mean square (RMS) of detail coefficients from time-synchronized voltage and frequency that reflect nonstationary occurrence of significant changes in signals. For a robust detection, wavelet-based detection parameter is designed with consideration of nonstationary characteristics of events. Also, there are distinct transients in voltage and frequency caused by different event types, and distinct results are key-idea of event classification. Besides the determination of event occurrences, one can obtain the information of event characteristics that include event types and zonal information of event from the proposed method. Moreover, successful results of detection and classification in real-world cases are presented in this paper.

Original language	English
Pages (from-to)	1154-1162
Number of pages	9
Journal	IEEE Transactions on Smart Grid
Volume	8
Issue number	3
DOIs	https://doi.org/10.1109/TSG.2015.2478421
Publication status	Published - 2017 May

Bibliographical note

Funding Information:
Manuscript received March 18, 2015; revised July 29, 2015 and August 30, 2015; accepted September 1, 2015. Date of publication October 6, 2015; date of current version April 19, 2017. This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015. (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea (e-mail: yongjune@yonsei.ac.kr).

Funding Information:
This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015.

Publisher Copyright:
© 2015 IEEE.

All Science Journal Classification (ASJC) codes

Computer Science(all)

Access to Document

10.1109/TSG.2015.2478421

Cite this

@article{edd16c14e3ca4d2384ee8009d8565214,

title = "Wavelet-based event detection method using PMU data",

abstract = "In order to deal with the nonstationary signatures of phasor measurement units (PMU) signals, this paper presents a wavelet-based detection algorithm. Moreover, for an application to PMU for event detection purpose, it is necessary for us to classify detected events into unexpected real power related accidents, such as generator trip or automated control, such as reactive power injection. The proposed normalized wavelet energy function calculates the root mean square (RMS) of detail coefficients from time-synchronized voltage and frequency that reflect nonstationary occurrence of significant changes in signals. For a robust detection, wavelet-based detection parameter is designed with consideration of nonstationary characteristics of events. Also, there are distinct transients in voltage and frequency caused by different event types, and distinct results are key-idea of event classification. Besides the determination of event occurrences, one can obtain the information of event characteristics that include event types and zonal information of event from the proposed method. Moreover, successful results of detection and classification in real-world cases are presented in this paper.",

author = "Kim, {Do In} and Chun, {Tae Yoon} and Yoon, {Sung Hwa} and Gyul Lee and Shin, {Yong June}",

note = "Funding Information: Manuscript received March 18, 2015; revised July 29, 2015 and August 30, 2015; accepted September 1, 2015. Date of publication October 6, 2015; date of current version April 19, 2017. This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015. (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea (e-mail: yongjune@yonsei.ac.kr). Funding Information: This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015. Publisher Copyright: {\textcopyright} 2015 IEEE.",

year = "2017",

month = may,

doi = "10.1109/TSG.2015.2478421",

language = "English",

volume = "8",

pages = "1154--1162",

journal = "IEEE Transactions on Smart Grid",

issn = "1949-3053",

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

number = "3",

}

TY - JOUR

T1 - Wavelet-based event detection method using PMU data

AU - Kim, Do In

AU - Chun, Tae Yoon

AU - Yoon, Sung Hwa

AU - Lee, Gyul

AU - Shin, Yong June

N1 - Funding Information: Manuscript received March 18, 2015; revised July 29, 2015 and August 30, 2015; accepted September 1, 2015. Date of publication October 6, 2015; date of current version April 19, 2017. This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015. (Corresponding author: Yong-June Shin.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea (e-mail: yongjune@yonsei.ac.kr). Funding Information: This work was supported in part by the National Research Foundation (NRF) of Korea through the Ministry of Science, Information and Communications Technologies, and Future Planning under Grant NRF-2014R1A2A1A01004780 and Grant NRF-2012M2A8A4055236, and in part by the Korea Electric Power Corporation Research Institute through the Korea Electrical Engineering and Science Research Institute under Grant R13TA20. Paper no. TSG-00317-2015. Publisher Copyright: © 2015 IEEE.

PY - 2017/5

Y1 - 2017/5

N2 - In order to deal with the nonstationary signatures of phasor measurement units (PMU) signals, this paper presents a wavelet-based detection algorithm. Moreover, for an application to PMU for event detection purpose, it is necessary for us to classify detected events into unexpected real power related accidents, such as generator trip or automated control, such as reactive power injection. The proposed normalized wavelet energy function calculates the root mean square (RMS) of detail coefficients from time-synchronized voltage and frequency that reflect nonstationary occurrence of significant changes in signals. For a robust detection, wavelet-based detection parameter is designed with consideration of nonstationary characteristics of events. Also, there are distinct transients in voltage and frequency caused by different event types, and distinct results are key-idea of event classification. Besides the determination of event occurrences, one can obtain the information of event characteristics that include event types and zonal information of event from the proposed method. Moreover, successful results of detection and classification in real-world cases are presented in this paper.

AB - In order to deal with the nonstationary signatures of phasor measurement units (PMU) signals, this paper presents a wavelet-based detection algorithm. Moreover, for an application to PMU for event detection purpose, it is necessary for us to classify detected events into unexpected real power related accidents, such as generator trip or automated control, such as reactive power injection. The proposed normalized wavelet energy function calculates the root mean square (RMS) of detail coefficients from time-synchronized voltage and frequency that reflect nonstationary occurrence of significant changes in signals. For a robust detection, wavelet-based detection parameter is designed with consideration of nonstationary characteristics of events. Also, there are distinct transients in voltage and frequency caused by different event types, and distinct results are key-idea of event classification. Besides the determination of event occurrences, one can obtain the information of event characteristics that include event types and zonal information of event from the proposed method. Moreover, successful results of detection and classification in real-world cases are presented in this paper.

UR - http://www.scopus.com/inward/record.url?scp=85027714916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85027714916&partnerID=8YFLogxK

U2 - 10.1109/TSG.2015.2478421

DO - 10.1109/TSG.2015.2478421

M3 - Article

AN - SCOPUS:85027714916

SN - 1949-3053

VL - 8

SP - 1154

EP - 1162

JO - IEEE Transactions on Smart Grid

JF - IEEE Transactions on Smart Grid

IS - 3

ER -

Wavelet-based event detection method using PMU data

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this