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Electrical Engineering and Systems Science > Systems and Control

arXiv:2108.08736 (eess)
[Submitted on 19 Aug 2021 (v1), last revised 19 Mar 2022 (this version, v2)]

Title:Forced Oscillation Identification and Filtering from Multi-Channel Time-Frequency Representation

Authors:Pablo Gill Estevez, Pablo Marchi, Francisco Messina, Cecilia Galarza
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Abstract:Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially in cases under resonance condition with natural system modes, where the magnitudes of the oscillations could be greater in places far from the source. Therefore, it is of interest to construct a global time-frequency (TF) representation (TFR) of the system, which can capture the oscillatory components present in the system. In this paper we develop a systematic methodology for frequency identification and component filtering of non-stationary power system forced oscillations (FO) based on multi-channel TFR. The frequencies of the oscillatory components are identified on the TF plane by applying a modified ridge estimation algorithm. Then, filtering of the components is carried out on the TF plane applying the anti-transform functions over the individual TFRs around the identified ridges. This step constitutes an initial stage for the application of the Dissipating Energy Flow (DEF) method used to locate FO sources. Besides, we compare three TF approaches: short-time Fourier transform (STFT), STFT-based synchrosqueezing transform (FSST) and second order FSST (FSST2). Simulated signals and signals from real operation are used to show that the proposed method provides a systematic framework for identification and filtering of power systems non-stationary forced oscillations.
Comments: Submitted to IEEE Transactions on Power Systems
Subjects: Systems and Control (eess.SY); Signal Processing (eess.SP)
Cite as: arXiv:2108.08736 [eess.SY]
  (or arXiv:2108.08736v2 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2108.08736

Submission history

From: Pablo Daniel Gill Estevez [view email]
[v1] Thu, 19 Aug 2021 15:07:43 UTC (4,235 KB)
[v2] Sat, 19 Mar 2022 22:01:03 UTC (7,824 KB)
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