Online Electromechanical Mode Identification from Power System Ringdown Responses

This paper introduces a comprehensive methodology for the online identification of electromechanical modes. Within the proposed framework, a series of tasks is executed prior to mode estimation in order to efficiently handle ringdown data. These tasks include detecting the onset of a disturbance, extracting the optimum length of the ringdown, and further pre-processing the signal for enhanced analysis. To perform mode identification the Hybrid frequency/time-domain approach is used. The performance of the proposed methodology is tested using a synthetic signal which is intentionally distorted with noise and contains non-typical data. Results indicate the applicability and efficiency of the proposed method for the online identification of power systems electromechanical modes.

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Published
2024-10-21
DOI
https://doi.org/10.1109/synergymed62435.2024.10799289
Primary Topic
Power Systems and Technologies
Type
article
Field-Weighted Citation Impact
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article

Online Electromechanical Mode Identification from Power System Ringdown Responses

Georgios A. Barzegkar‐Ntovom, Efthimios P. Saroudis, Eleftherios O. Kontis, Grigoris K. Papagiannis et al.
Power Systems and Technologies
article

Online Electromechanical Mode Identification from Power System Ringdown Responses

Georgios A. Barzegkar‐Ntovom, Efthimios P. Saroudis, Eleftherios O. Kontis, Grigoris K. Papagiannis, Theofilos A. Papadopoulos, Achilleas I. Sfetkos
article en

Abstract

This paper introduces a comprehensive methodology for the online identification of electromechanical modes. Within the proposed framework, a series of tasks is executed prior to mode estimation in order to efficiently handle ringdown data. These tasks include detecting the onset of a disturbance, extracting the optimum length of the ringdown, and further pre-processing the signal for enhanced analysis. To perform mode identification the Hybrid frequency/time-domain approach is used. The performance of the proposed methodology is tested using a synthetic signal which is intentionally distorted with noise and contains non-typical data. Results indicate the applicability and efficiency of the proposed method for the online identification of power systems electromechanical modes.

University of Thessaly (GR), Democritus University of Thrace (GR), Aristotle University of Thessaloniki (GR)
Hellenic Foundation for Research and Innovation
Openalex Percentile: Top 78%
Power Systems and Technologies
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