DFT-Based Identification of Oscillation Modes from PMU Data Using an Exponential Window Function
Abstract
:1. Introduction
2. Conventional Modal Identification in Power Systems
2.1. Models for Modal Identification in Time and Frequency Domains
2.2. Conventional DFT-Based Method
2.3. Prony Method with SVD
3. Modal Identification Methods with Exponential Window Function
3.1. Exponential Window Function for Modal Identification
3.2. Simulations through Synthetic Data
4. Identification of Oscillation modes from PMU Data
4.1. Interarea Mode of Central American Power System
4.2. Regional Mode of New England Power System
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Hwang, J.K.; Seo, S.; Castanon, J.S.; Kim, H.-C. DFT-Based Identification of Oscillation Modes from PMU Data Using an Exponential Window Function. Energies 2019, 12, 4357. https://doi.org/10.3390/en12224357
Hwang JK, Seo S, Castanon JS, Kim H-C. DFT-Based Identification of Oscillation Modes from PMU Data Using an Exponential Window Function. Energies. 2019; 12(22):4357. https://doi.org/10.3390/en12224357
Chicago/Turabian StyleHwang, Jin Kwon, Sangsoo Seo, Julian Sotelo Castanon, and Ho-Chan Kim. 2019. "DFT-Based Identification of Oscillation Modes from PMU Data Using an Exponential Window Function" Energies 12, no. 22: 4357. https://doi.org/10.3390/en12224357