Abstract
In this paper, a multiscale compression process for phasor a measurement unit (PMU) is proposed using a wide-area event detection method. For the first step, the data compression intervals are adaptively selected by monitoring the average of modified wavelet energy (AMWE) in order to reflect two different operating conditions of power system; i.e., ambient and event. In the next step, the interval-selected dataset is compressed by a multiscale dimensionality reduction process. The dimensionality reduction step uses wavelet decomposition to reflect non-stationary characteristics and extract time-varying features from the PMU signals. The principal component analysis is then applied to the wavelet-decomposed matrices for data compression. The effectiveness of the proposed method was confirmed by application to the real-world PMU voltage and frequency data, and comparisons are made with the conventional wavelet compression technique.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 437-442 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538640555 |
| DOIs | |
| Publication status | Published - 2018 Apr 17 |
| Event | 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017 - Dresden, Germany Duration: 2017 Oct 23 → 2017 Oct 26 |
Publication series
| Name | 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017 |
|---|---|
| Volume | 2018-January |
Other
| Other | 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017 |
|---|---|
| Country | Germany |
| City | Dresden |
| Period | 17/10/23 → 17/10/26 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Safety, Risk, Reliability and Quality
Cite this
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Multiscale PMU data compression based on wide-area event detection. / Lee, Gyul; Shin, Yong June.
2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 437-442 (2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017; Vol. 2018-January).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Multiscale PMU data compression based on wide-area event detection
AU - Lee, Gyul
AU - Shin, Yong June
PY - 2018/4/17
Y1 - 2018/4/17
N2 - In this paper, a multiscale compression process for phasor a measurement unit (PMU) is proposed using a wide-area event detection method. For the first step, the data compression intervals are adaptively selected by monitoring the average of modified wavelet energy (AMWE) in order to reflect two different operating conditions of power system; i.e., ambient and event. In the next step, the interval-selected dataset is compressed by a multiscale dimensionality reduction process. The dimensionality reduction step uses wavelet decomposition to reflect non-stationary characteristics and extract time-varying features from the PMU signals. The principal component analysis is then applied to the wavelet-decomposed matrices for data compression. The effectiveness of the proposed method was confirmed by application to the real-world PMU voltage and frequency data, and comparisons are made with the conventional wavelet compression technique.
AB - In this paper, a multiscale compression process for phasor a measurement unit (PMU) is proposed using a wide-area event detection method. For the first step, the data compression intervals are adaptively selected by monitoring the average of modified wavelet energy (AMWE) in order to reflect two different operating conditions of power system; i.e., ambient and event. In the next step, the interval-selected dataset is compressed by a multiscale dimensionality reduction process. The dimensionality reduction step uses wavelet decomposition to reflect non-stationary characteristics and extract time-varying features from the PMU signals. The principal component analysis is then applied to the wavelet-decomposed matrices for data compression. The effectiveness of the proposed method was confirmed by application to the real-world PMU voltage and frequency data, and comparisons are made with the conventional wavelet compression technique.
UR - http://www.scopus.com/inward/record.url?scp=85048515853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048515853&partnerID=8YFLogxK
U2 - 10.1109/SmartGridComm.2017.8340705
DO - 10.1109/SmartGridComm.2017.8340705
M3 - Conference contribution
AN - SCOPUS:85048515853
T3 - 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017
SP - 437
EP - 442
BT - 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -