Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement

Lixin Wang; Weijun Suo; Han Gao; Zhenglong Sun; Shiwei Xia; Tek Tjing Lie

doi:10.35833/MPCE.2025.000811

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Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement

Original Paper | 更新时间：2026-09-28

- Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement
- Journal of Modern Power Systems and Clean Energy Vol. 14, Issue 5, Pages: 1622-1633(2026)
- 作者机构：
  
  1.Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China
  2.School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, China
  3.China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China
  4.Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand
- 作者简介：
  
  Lixin Wang received the B.S., M.S., and Ph.D. degrees in electrical engineering from Northeast Electric Power University, Jilin, China, in 2014, 2017, and 2021, respectively. From 2024 to 2025, she was a Post-doctoral Fellow with Northeast Electric Power University. She was also a Visiting Scholar with Auckland University of Technology, Auckland, New Zealand, in 2025. She is currently an Associate Professor with the School of Electrical Engineering, Northeast Electric Power University. Her research interests include power system stability analysis and control, and data-driven situational awareness for renewable power system. Email:wanglxnedu@163.com
  Weijun Suo received the B.S. degree in electrical engineering and automation from Beihua University, Jilin, China, in 2024. He is currently working toward the M.S. degree in the School of Electrical Engineering, Northeast Electric Power University, Jilin, China. His research interests include power system stability analysis and control. Email:13965260932@163.com
  Han Gao received the B.S., M.S., and Ph.D. degrees in electrical engineering from Northeast Electric Power University, Jilin, China, in 2017, 2020, and 2023, respectively. She is currently a Lecturer with Harbin University of Science and Technology, Harbin, China. Her research interests include machine learning, and data-driven power system stability analysis and control. Email:Hannah0323gh@hotmail.com
  Zhenglong Sun received the B.S., M.S., and Ph.D. degrees in electrical engineering from Northeast Electric Power University, Jilin, China, in 2011, 2014, and 2018, respectively. He is currently a Professor with the School of Electrical Engineering, Northeast Electric Power University. His research interests include stability analysis and control of renewable power system. Email:nedusunzl@ neepu.edu.cn
  Shiwei Xia received the Ph.D. degree in power system and its automation from The Hong Kong Polytechnic University, Hong Kong, China, in 2015. He is currently an Associate Professor with the School of Electrical Engineering, North China Electric Power University, Beijing, China. His research interests include renewable power system operation and control. Email:s.w.xia@ncepu.edu.cn
  Tek Tjing Lie received the B.S. degree in electrical engineering from Oklahoma State University, Stillwater, USA, in 1986, and the M.S. and Ph.D. degrees in electrical engineering from Michigan State University, East Lansing, USA, in 1988 and 1992, respectively. He is currently a Full Professor and the Head of the School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand. His research interests include power system operation and control, deregulated electricity market, and artificial intelligence application to power system, power electronic, renewable energy, and smart grid. Email:tek.lie@aut.ac.nz
- 基金信息：
- DOI：10.35833/MPCE.2025.000811
  CLC：
- Received：31 August 2025，
  
  Revised：2025-12-02，
  
  Accepted：29 January 2026，
  
  Online First：30 March 2026，
  
  Published：15 September 2026
- 稿件说明：
移动端阅览
Wang Lixin,Suo Weijun,Gao Han,et al.Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1622-1633.
DOI：

Wang Lixin,Suo Weijun,Gao Han,et al.Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement[J].Journal of Modern Power Systems and Clean Energy,2026,14(05):1622-1633. DOI： 10.35833/MPCE.2025.000811.

摘要

Abstract

Forced oscillation source localization (FOSL) using synchrophasor measurements is critical for mitigating forced oscillations (FOs) in power systems. However

the performance of the conventional dissipating energy flow (DEF) is significantly affected by measurement noise and other irrelevant modal components. To address this challenge

an optimal subspace-enhanced (Os-enhanced) dynamic mode decomposition (DMD)-assisted DEF-based FOSL is proposed in this paper using synchrophasor measurement. First

Os-enhanced DMD is employed to decompose multi-channel measurements into time-domain responses of individual modes. Then

the time-domain components associated with FO are distinguished from the decomposed modes based on the rate of change of modal energy

and are subsequently used to calculate the DEF at all generator buses. Furthermore

the rate of forced energy is introduced as an indicator of energy flow direction to localize the FO source. The performance of the Os-enhanced DMD-assisted DEF is evaluated by the simulated data from IEEE 16-machine 5-area system and field phasor measurement unit (PMU) measurement data from Independent System Operator New England (ISO-NE). Comparative results demonstrate that the Os-enhanced DMD-assisted DEF achieves improved accuracy and robustness for localizing FO sources under noisy conditions.

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Keywords

references

H. Li, P. Ju, C. Gan et al. , “Analytic estimation method of forced oscillation amplitude under stochastic continuous disturbances,” IEEE Transactions on Smart Grid , vol. 10 , no. 4 , pp. 4026 - 4036 , Jul. 2019 .

A. Silverstein, “Diagnosing equipment health and mis-operations with PMU data,” NASPI, Washington DC, USA, Tech. Rep. NASPI-2015-TR-009, May 2015.

NERC, “January 11, 2019 eastern interconnection oscillation event,” North American Electric Reliability Corporation, Atlanta, USA, Tech. Rep. Dec. 2019.

L. Zhu, W. Yu, Z. Jiang et al. , “A comprehensive method to mitigate forced oscillations in large interconnected power grids,” IEEE Access , vol. 9 , pp. 22503 - 22515 , Feb. 2021 .

S. A. N. Sarmadi and V. Venkatasubramanian, “Inter-area resonance in power systems from forced oscillations,” IEEE Transactions on Power Systems , vol. 31 , no. 1 , pp. 378 - 386 , Jan. 2016 .

X. Wang and K. Turitsyn, “Data-driven diagnostics of mechanism and source of sustained oscillations,” IEEE Transactions on Power Systems , vol. 31 , no. 5 , pp. 4036 - 4046 , Sept. 2016.

R. T. Alden and A. A. Shaltout, “Analysis of damping and synchronizing torques Part I-A general calculation method,” IEEE Transactions on Power Apparatus and Systems , vol. PAS-98, no. 5 , pp. 1696 - 1700 , Sept. 1979.

A. Shaltout and R. T. Alden, “Analysis of damping and synchronizing torques part II: effect of operating conditions and machine parameters,” IEEE Transactions on Power Apparatus and Systems , vol. PAS-98, no. 5 , pp. 1701 - 1708 , Sept. 1979.

Y. Meng, Z. Yu, N. Lu et al. , “Time series classification for locating forced oscillation sources,” IEEE Transactions on Smart Grid , vol. 12 , no. 2 , pp. 1712 - 1721 , Mar. 2021 .

Z. Cui, W. Hu, G. Zhang et al. , “Knowle dge-informed deep learning method for multiple oscillation sources localization,” IEEE Transactions on Power Systems , vol. 40 , no. 3 , pp. 2811 - 2814 , May 2025.

L. Chen, Y. Min, and W. Hu, “An energy-based method for location of power system oscillation source,” IEEE Transactions on Power Systems , vol. 28 , no. 2 , pp. 828 - 836 , May 2013.

S. Maslennikov, “Enhancing the efficiency of locating the oscillation source,” IEEE Transactions on Power Systems , vol. 39 , no. 6 , pp. 7257 - 7265 , Nov. 2024 .

T. Jiang, N. Ye, and Q. Li, “Time-frequency domain dissipation energy flow method for forced oscillation source location in power system,” Automation of Electric Power Systems , vol. 48 , no. 19 , pp. 120 - 128 , Jul. 2024 .

A. O. Boudraa and J. C. Cexus, “EMD-based signal filtering,” IEEE Transactions on Instrumentation and Measurement , vol. 56 , no. 6 , pp. 2196 - 2202 , Dec. 2007 .

K. Hong, L. Wang, and S. Xu, “A variational mode decomposition approach for degradatio n assessment of power transformer windings,” IEEE Transactions on Instrumentation and Measurement , vol. 68 , no. 4 , pp. 1221 - 1229 , Apr. 2019 .

L. Chen, M. Sun, Y. Min et al. , “Online monitoring of generator damping using dissipation energy flow computed from ambient data,” IET Generation, Transmission & Distribution , vol. 11 , no. 18 , pp. 4430 - 4435 , Mar. 2017 .

P. G. Estevez, P. Marchi, C. Galarza et al. , “Non-stationary power system forced oscillation analysis using synchrosqueezing transform,” IEEE Transactions on Power Systems , vol. 36 , no. 2 , pp. 1583 - 1593 , Mar. 2021 .

T. Jiang, B. Liu, G. Liu et al. , “Forced oscillation source location of bulk power systems using synchrosqueezing wavelet transform,” IEEE Transactions on Power Systems , vol. 39 , no. 5 , pp. 6689 - 6701 , Sept. 2024.

T. Jiang, B. Liu, X. Li et al. , “Forced oscillation location in power systems using multiple empirical mode decomposition,” Proceedings of the Chinese Society of Electrical Engineering , vol. 42 , no. 22 , pp. 8063 - 8074 , Dec. 2022 .

T. Jiang, B. Liu, B. Wang et al. , “Forced oscillation source location in power systems using MVMD-assisted DEF in TF plane,” IEEE Transactions on Power Systems , vol. 39 , no. 6 , pp. 6901 - 6913 , Nov. 2024 .

X. Wang and K. Turitsyn, “Data-driven diagnostics of mechanism and source of sustained oscillations,” IEEE Transactions on Power Systems , vol. 31 , no. 5 , pp. 4036 - 4046 , Sept. 2016.

D. Yang, H. Gao, G. Cai et al. , “Synchronized ambient data-based extraction of interarea modes using Hankel block-enhanced DMD,” International Journal of Electrical Power and Energy Systems , vol. 128 , pp. 1 - 9 , Jun. 2021 .

J. Tu, C. W. Rowley, D. M. Luchtenburg et al. , “On dynamic mode decomposition: theory and applications,” Journal of Computational Dynamics , vol. 1 , no. 2 , pp. 391 - 421 , Jun. 2014 .

G. H. Golub and C. F. Van Loan, Matrix Computations . Baltimore: Johns Hopkins University Press, 1996, pp. 281 - 280 .

P. J. Goulart and A. Wynn, “Optimal mode decomposition for high dimensional systems,” in Proceedings of Conference on Decision and Control , Maui, USA, Feb. 2012 , pp. 4965 - 4970 .

P. Benner, C. Himpe, and T. Mitchell, “On reduced input-output dynamic mode decomposition,” Advances in Computational Mathematics , vol. 44 , pp. 1687 - 1716 , Feb. 2018 .

T. Askham and J. N. Kutz, “Variable-projection methods for an optimized dynamic mode decomposition,” SIAM Journal on Applied Dynamical Systems , vol. 17 , no. 2 , pp. 1234 - 1265 , Feb. 2018 .

A. Edelman, T. A. Arias, and S. T. Smith, “The geometry of algorithms with orthogonality constraints,” SIAM Journal on Matrix Analysis and Applications , vol. 20 , no. 2 , pp. 303 - 353 , Apr. 1998 .

X. Chen, L. Huo, and C. Sun, “Deep-learning-based short-term voltage stability assessment with topology-adaptive voltage dynamic feature and domain transfer,” Journal of Modern Power Systems and Clean Energy , vol. 13 , no. 5 , pp. 1545 - 1555 , Sept. 2025.

X. Zhang, W. Yan, M. Huo et al ., “Robust interval state estimation for distribution systems considering pseudo-measurement interval prediction,” Journal of Modern Power Systems and Clean Energy , vol. 12 , no. 1 , pp. 179 - 188 , Jan. 2024 .

C. Canizares, T. Fernandes, E. Geraldi et al ., “Benchmark models for the analysis and control of small-signal oscillatory dynamics in power systems,” IEEE Transactions on Power Systems , vol. 32 , no. 1 , pp. 715 - 722 , Jan. 2017 .

S. Maslennikov, K. Sun, B. Wang. (2020, Dec.). Test cases library of power system sustained oscillations. [Online]. Available: http://web.eecs.utk.edu/kaisun/Oscillation/index.html

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