Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations

Peng, Jimmy Chih-Hsien; Nair, Nirmal-Kumar C.

doi:10.1109/tpwrs.2011.2169284

Cited by 60 publications

(36 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The identification of the bisecting frequencies ω bi ∈ [ω i , ω i + 1 ], for i = 1, 2, …, m − 1 can be performed by determining the m peaks of the L p periodogram of the signal (with 1 < p < 2). Each elemental signal obtained by (12) has the feature of being mono-frequency. By making use of the HT fundamentals reported in Section 2, the instantaneous amplitude and phase angle values of the ith component y d i (t) can be determined.…”

Section: Lfo Identificationmentioning

confidence: 99%

See 1 more Smart Citation

On Hilbert transform methods for low frequency oscillations detection

Lauria¹,

Pisani²

2014

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

This study tackles the issue of electromechanical modes identification through a measurement-based methodology employing a novel signal decomposition theorem based upon the Hilbert transform. The methodology aims to answer in a simpler and more pragmatic manner to the main weaknesses of the Hilbert-Huang transform with respect to the major refinements in the relevant literature. These weak points are discussed with sufficient detailed degree in the study. The main contribution of this study consists in combining a recent signal decomposition theorem for separating an assigned signal into elemental ones, each of them characterised by a single frequency component and a robust preliminary non-linear spectral analyser, named Lp periodogram. This procedure's results are very appropriate for analysing some critical cases of electromechanical oscillations, because of the Lp periodogram robustness against heavy-tailed noise and also its intrinsic ability in estimating closely spaced frequency components. The proposed approach is found to be inherently simple, reliable and consistent in performance as well as characterised by low computational burden. Some numerical applications validate the methodology and assess its own performance on synthetic signals, near real-life signals acquired by IEEE test networks and on a real measured signal from a wide-area monitoring system currently in operation

show abstract

Section: Lfo Identificationmentioning

confidence: 99%

“…Conversely, the modified version performs a bisection of the previously decomposed signals in sequence y(t) = s 1 (t) + s 1 (t); s 1 (t) = s 2 (t) + s 2 (t); s m−2 (t) = s m−1 (t) + s m−1 (t) (46) with the elemental signals of the (12) in the body of the work y d 1 (t) = s i (t) for i = 1, 2, 3, . .…”

Section: Acknowledgmentsmentioning

confidence: 99%

On Hilbert transform methods for low frequency oscillations detection

Lauria¹,

Pisani²

2014

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…According to the current state to calculate the next optimal estimation valuê, and update the system covariance . (17) Every time the optimized estimate value ̂o f moment is obtained, the Kalman filter algorithm takes ̂a nd as exist conditions, and repeat these steps to make operation continuous [17][18][19][20][21][22].…”

Section: Figure 8 Action Time Of Latching Relaymentioning

confidence: 99%

A Novel Synchronous Switch Method Based on Kalman Filter

Gui-lin¹,

Chen²

2014

IJFGCN

View full text Add to dashboard Cite

show abstract

“…Several methods have been developed to assess the small signal stability, among which the mode estimation is the most efficient method. It can provide vital information about small signal stability via approaches which are based on model simulation and measurement data [3][4][5][6][7][8][9][10][11]. The system modes are the eigenvalues of the linear model of the system.…”

Section: Introductionmentioning

confidence: 99%

“…To complement mode estimation approaches, wide-area measurement-based approaches by using dominant-time data from phasor measurement units (PMU) are applied extensively, based on signal processing and pattern recognition techniques such as Prony [7][8][9], total least square-estimation of signal parameters via rotational invariance technique (TLS-ESPRIT) [10] and auto regressive moving average (ARMA) [11,12]. Prony and its extended methods have become a typical practice to extract the modes from the measured data without the detailed power system model and accurate parameters.…”

Section: Introductionmentioning

confidence: 99%

Mode matching pursuit for estimating dominant modes in bulk power grid

Jiang

Jia

Zhao

et al. 2014

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

This study presents a general approach to identify dominant oscillation modes in bulk power system by using wide-area measurement system. To automatically identify the dominant modes without artificial participation, spectral characteristic of power system oscillation mode is applied to distinguish electromechanical oscillation modes which are calculated by stochastic subspace method, and a proposed mode matching pursuit is adopted to discriminate the dominant modes from the trivial modes, then stepwise-refinement scheme is developed to remove outliers of the dominant modes and the highly accurate dominant modes of identification are obtained. The method is implemented on the dominant modes of China Southern Power Grid which is one of the largest AC/DC paralleling grids in the world. Simulation data and fieldmeasurement data are used to demonstrate high accuracy and better robustness of the dominant modes identification approach.

show abstract

Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations

Cited by 60 publications

References 32 publications

On Hilbert transform methods for low frequency oscillations detection

On Hilbert transform methods for low frequency oscillations detection

A Novel Synchronous Switch Method Based on Kalman Filter

Mode matching pursuit for estimating dominant modes in bulk power grid

Contact Info

Product

Resources

About