TY - JOUR
T1 - Oscillation Recognition Using a Geometric Feature Extraction Process Based on Periodic Time-Series Approximation
AU - Cho, Hwanhee
AU - Choi, Namki
AU - Lee, Byongjun
N1 - Funding Information:
This work was supported by Korea Electric Power Corporation (Grant number: R17XA05-4) and the Human Resource Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), with financial resources from the Ministry of Trade, Industry, and Energy, Republic of Korea (No. 20174030201820).
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincaré map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincaré map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.
AB - Oscillations may cause both economic and technical problems such as a reduction in overall system reliability. Therefore, detecting and preventing oscillatory behavior that affects power systems is important. This paper proposes an oscillation recognition method that includes monitoring and extracting features in a recursive and sequential manner in a time-series measurement in power systems. We propose a geometric feature extraction process for recognizing oscillations by constructing an average system and Poincaré map for time-series measurement. The proposed process provides the features of a system's damping and frequency of oscillation, and the developed monitoring systems are based on nonlinear dynamics. The circulating oscillatory behavior is represented on a finite-integer-delay embedded time-series plane, extracted by a Poincaré map construction, and examined directly along the trajectory to monitor the features of the oscillation according to damping and frequency. Oscillatory behavior recognition is tested on IEEE's second benchmark system for subsynchronous resonance to verify the fast extraction of oscillation components. In addition, a case study for Korean power systems with a high penetration of renewable energy and application on actual measurement data is carried out to demonstrate the practical application of the process.
KW - Approximation method
KW - oscillation monitoring
KW - power system measurement
KW - subsynchronous oscillation
KW - time-series analysis
UR - http://www.scopus.com/inward/record.url?scp=85080869311&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.2974259
DO - 10.1109/ACCESS.2020.2974259
M3 - Article
AN - SCOPUS:85080869311
VL - 8
SP - 34375
EP - 34386
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9000587
ER -
