Synergistic data analytics for electromechanical oscillation in electric power systems

Jiang, Tao; Kou, Xiao; Liu, Guodong; Yuan, Haoyu

doi:10.1016/j.ijepes.2021.107610

Title: Synergistic data analytics for electromechanical oscillation in electric power systems

Journal Article · 17 September 2021 · International Journal of Electrical Power and Energy Systems

DOI: https://doi.org/10.1016/j.ijepes.2021.107610 · OSTI ID:1883776

Jiang, Tao ^[1]; Kou, Xiao ^[2];

^[3]; Yuan, Haoyu ^[2]

Northeast Electric Power Univ., Jilin (China)
Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Accurate real-time estimation of the four electromechanical oscillation properties, i.e., dominant oscillation modes, mode shapes, participation factors, and coherent groups, is of great importance to assess and mitigate potential electromechanical oscillations in interconnected power systems. While eigenvalue analysis can realize such estimation, it requires precise linearized dynamic models and accurate parameters, which are highly difficult to obtain in practice. Data fusion-based modal estimation methods can extract the properties of electromechanical oscillations from measurement data without the power system model and parameters, but most of the time only one or two property assessments can be accomplished each time. To overcome this challenge, this paper presents a synergistic data analytics solution to characterize the dynamic behaviors of electromechanical oscillations from real-time measurement data. Here, the proposed method uses optimized variable projection, and it is capable of estimating all four electromechanical oscillation behavior properties from measured responses. Case studies are performed using the simulated measurement data of a 16-generator 68-bus test system and the field measurements collected by the PMUs deployed in the Yunnan Power Grid. The results demonstrate that the proposed synergistic data analytics solution can achieve satisfactory performance in capturing the properties of electromechanical oscillations from measurement data and exhibit strong robustness against measurement noise when compared with existing measurement-based methods.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Natural Science Foundation of China (NSFC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1883776

Journal Information:: International Journal of Electrical Power and Energy Systems, Journal Name: International Journal of Electrical Power and Energy Systems Journal Issue: 1 Vol. 135; ISSN 0142-0615

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Synergistic data analytics for electromechanical oscillation in electric power systems

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