Analysis of Oscillations with Eigenanalysis and Prony Techniques

Koessler, R.J.; Prabhakara, F.S.; Al-Mubarak, Ahmed H.

doi:10.1109/pes.2007.385704

Cited by 15 publications

(5 citation statements)

References 4 publications

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“…Using a multi-resolution series of DWT's a mother wavelet (Fig. 2) is scaled in the frequency domain by m and shifted in the time domain by n (8).…”

Section: A Wavelet Transformmentioning

confidence: 99%

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Analysis of fixed-speed wind farm low-frequency power pulsations using a wavelet-Prony method

McSwiggan

Littler

2010

IEEE PES General Meeting

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The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency periodic pulsations contained within the active power flow from different wind farms. A primary concern is excitation of existing low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of the interconnected Northern and Southern power system networks. Recently grid code requirements on the Northern Ireland power system have been updated stipulating that wind farms connected after 2005 must be able to control the magnitude of oscillations in the range of 0.25 -1.75 Hz to within 1% of the wind farm's registered output. In order to determine whether wind farm low-frequency oscillations have a negative effect (excite other modes) or possibly a positive impact (damping of existing modes) on the power system, the oscillations at the point of connection must be measured and characterised. Using time -frequency methods, research presented in this paper has been conducted to extract signal features from measured lowfrequency active power pulsations produced by wind farms to determine the effective composition of possible oscillatory modes which may have a detrimental effect on system dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

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“…Using a multi-resolution series of DWT's a mother wavelet (Fig. 2) is scaled in the frequency domain by m and shifted in the time domain by n (8).…”

Section: A Wavelet Transformmentioning

confidence: 99%

“…A more recent technique for time-frequency analysis has emerged in the form of the Prony method [8,9,10]. The method can provide a linear prediction model of a continuous signal by fitting to it a sum of exponential functions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of fixed-speed wind farm low-frequency power pulsations using a wavelet-Prony method

McSwiggan

Littler

2010

IEEE PES General Meeting

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“…Although a lot of research attention has been focused on damping and frequency estimation, not many papers exist on mode-shape estimation especially, in near real-time [16]. Identification of mode-shapes in ringdown scenario has been carried out with Matrix Pencil [17], Prony [18], momentmatching [19] and principal component analysis (PCA) [20] techniques. Small-signal continuous modal parameter estimation technique was used in [21].…”

Section: Introductionmentioning

confidence: 99%

Damping and relative mode-shape estimation in near real-time through phasor approach

Chaudhuri

2011

2011 IEEE Power and Energy Society General Meeting

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A technique for estimating damping and electromechanical mode-shape in near real-time as oscillations develop under transient condition is presented. At each sampling instant, measured signals are expressed as phasors using corrected values of modal frequencies. Damping is obtained from the exponential variation of estimated phasor magnitude using a moving window least squares (LS) algorithm. The relative mode-shape is computed directly from the magnitude and phase angle of the phasors. Random variations in loads are considered to examine possible impact on phasor estimation, especially the frequency correction loop. Accuracy and speed of convergence is validated by comparing the time variation of estimated dampings and relative mode-shapes against the actual values obtained from the linearized models under respective operating conditions. Besides the well-known four-machine, two-area test system, a 16-machine, five-area system is considered for illustration of the concept. Monte Carlo simulations are used to capture the statistical variability in estimation as a result of persistent disturbances (e.g., random fluctuations in loads) leading to different signal-to-noise ratios (SNRs). Results from a commercial real-time simulator illustrate the practical feasibility of the proposed approach.

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“…Identification of mode-shapes in ringdown scenario has been carried out with Matrix Pencil [17], Prony [18], momentmatching [19] and principal component analysis (PCA) [20] techniques. Small-signal continuous modal parameter estimation technique was used in [21].…”

mentioning

confidence: 99%

Damping and Relative Mode-Shape Estimation in Near Real-Time Through Phasor Approach

Chaudhuri

2011

IEEE Trans. Power Syst.

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Abstract-A technique for estimating damping and electromechanical mode-shape in near real-time as oscillations develop under transient condition is presented. At each sampling instant measured signals are expressed as phasors using corrected values of modal frequencies. Damping is obtained from the exponential variation of estimated phasor magnitude using a moving window Least Squares (LS) algorithm. The relative mode-shape is computed directly from the magnitude and phase angle of the phasors. Random variations in loads are considered to examine possible impact on phasor estimation especially, the frequency correction loop. Accuracy and speed of convergence is validated by comparing the time variation of estimated dampings and relative mode-shapes against the actual values obtained from the linearized models under respective operating conditions. Besides the well-known 4-machine, 2-area test system, a 16-machine, 5-area system is considered for illustration of the concept. MonteCarlo simulations are used to capture the statistical variability in estimation as a result of persistent disturbances (e.g.random fluctuations in loads) leading to different signal-to-noise ratios (SNRs). Results from a commercial real-time simulator illustrate the practical feasibility of the proposed approach.

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Analysis of Oscillations with Eigenanalysis and Prony Techniques

Cited by 15 publications

References 4 publications

Analysis of fixed-speed wind farm low-frequency power pulsations using a wavelet-Prony method

Analysis of fixed-speed wind farm low-frequency power pulsations using a wavelet-Prony method

Damping and relative mode-shape estimation in near real-time through phasor approach

Damping and Relative Mode-Shape Estimation in Near Real-Time Through Phasor Approach

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