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
- Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement
- MPCE 2026年14卷第5期页码：1622-1633
- 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：
  
  the National Natural Science Foundation of China(52507083);the State Key Laboratory of Power System Operation and Control(SKLD25KM36)
- DOI：10.35833/MPCE.2025.000811
  中图分类号：
- 收稿：2025-08-31，
  
  修回：2025-12-02，
  
  录用：2026-01-29，
  
  网络首发：2026-03-30，
  
  纸质出版：2026-09-15
- 稿件说明：
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Lixin Wang, Weijun Suo, Han Gao, 等. Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement[J]. MPCE, 2026,14(5):1622-1633.

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.
Lixin Wang, Weijun Suo, Han Gao, 等. Optimal Subspace-enhanced Dynamic Mode Decomposition-assisted Dissipating Energy Flow-based Forced Oscillation Source Localization Using Synchrophasor Measurement[J]. MPCE, 2026,14(5):1622-1633. DOI： 10.35833/MPCE.2025.000811.

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.

关键词

Keywords

references

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