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A method for the identification of low frequency oscillation modes in power systems subjected to noise

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  • Jin, Tao
  • Liu, Siyi
  • Flesch, Rodolfo C.C.
  • Su, Wencong

Abstract

The high penetration of renewable energy sources and the consequent integration of such distributed energy generation systems into the grid have significantly increased the interaction between power sources, which can result in low frequency oscillations. Different control techniques are proposed in literature to suppress low frequency oscillations, but the first step to attenuate such oscillations is to characterize them in terms of their dominant modes. This paper combines different results from literature to propose a unified two-step methodology for the mode characterization of low frequency oscillations based on the signals acquired by wide area measurement systems. Since the measured signals typically contain noise, the first stage of the proposed method uses the basis pursuit denoising method combined with a Tunable Q-factor wavelet transform to increase the signal-to-noise ratio and improve the identification results. In a second stage, an improved version of the matrix pencil algorithm, as proposed in this paper, is used to identify the parameters of low frequency oscillation dominant modes. Both simulation and experimental results show that the proposed method has better characterization accuracy than traditional methods, especially when Gaussian noise is considered in the measurements. In addition, the processing time has proven to be reasonable for online identification and characterization of low frequency oscillations.

Suggested Citation

  • Jin, Tao & Liu, Siyi & Flesch, Rodolfo C.C. & Su, Wencong, 2017. "A method for the identification of low frequency oscillation modes in power systems subjected to noise," Applied Energy, Elsevier, vol. 206(C), pages 1379-1392.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:1379-1392
    DOI: 10.1016/j.apenergy.2017.09.123
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    References listed on IDEAS

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    2. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    3. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    4. Yang, Weijia & Norrlund, Per & Bladh, Johan & Yang, Jiandong & Lundin, Urban, 2018. "Hydraulic damping mechanism of low frequency oscillations in power systems: Quantitative analysis using a nonlinear model of hydropower plants," Applied Energy, Elsevier, vol. 212(C), pages 1138-1152.
    5. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    6. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
    7. Chen, Lei & Xie, Xiaorong & Li, Xiang & Yang, Lei & Cao, Xin, 2023. "Online SSO stability analysis-based oscillation parameter estimation in converter-tied grids," Applied Energy, Elsevier, vol. 351(C).

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