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.
|Title of host publication||2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|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
|Name||2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017|
|Other||2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017|
|Period||17/10/23 → 17/10/26|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning #NRF-2017R1A2A1A05001022. This research was also supported by the Korea Electrotechnology Research Institute (KERI) primary research program through the National Research Council of Science & Technology (NST) funded by the ministry of Science, ICE and Future Planning (MSIP) (No. 17-12-N0101-02).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Energy Engineering and Power Technology
- Safety, Risk, Reliability and Quality