2017
DOI: 10.1016/j.ijepes.2017.04.004
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Oscillation mode analysis for power grids using adaptive local iterative filter decomposition

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Cited by 16 publications
(7 citation statements)
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“…A Bayesian approach is also developed to identify the modal parameters based on their uncertainties using ambient PMU measurements [26]. Other improved methods for oscillation identification are also developed including adaptive local iterative filtering decomposition [27], digital Taylor–FT [28], extended Kalman filter [29], and adaptive stochastic subspace identification [30].…”
Section: Introductionmentioning
confidence: 99%
“…A Bayesian approach is also developed to identify the modal parameters based on their uncertainties using ambient PMU measurements [26]. Other improved methods for oscillation identification are also developed including adaptive local iterative filtering decomposition [27], digital Taylor–FT [28], extended Kalman filter [29], and adaptive stochastic subspace identification [30].…”
Section: Introductionmentioning
confidence: 99%
“…The FT spectrum indicates two oscillation modes in the active power signal, namely, P-1 and P-2. The adaptive local iterative filter decomposition (ALIFD) algorithm was utilized to identify the oscillation's characteristic parameters (frequency and damping ratio) [28]. As shown in Table 2, the frequencies of P-1 and P-2 were 0.6579 and 1.2651 Hz, respectively; both values were within the range of the electromechanical bandwidth.…”
Section: Simulation and Analysismentioning
confidence: 99%
“…Finally, Wang et al [18] investigate on the dynamic response of a grid‐tied voltage source converter during electromechanical oscillations, using the adaptive local iterative filter decomposition presented by Yang et al [19] to identify oscillation parameters.…”
Section: Introductionmentioning
confidence: 99%