Oscillatory Stability Limit Prediction Using Stochastic Subspace Identification

Ghasemi, Hassan; Cañizares, Claudio A.; Moshref, A.

doi:10.1109/tpwrs.2006.873100

Cited by 114 publications

(51 citation statements)

References 17 publications

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“…A relatively new method for output-only mode identification, refereed to as Frequency Domain Decomposition (FDD), has also been applied for mode estimation in power systems [10]. Another approach for mode estimation that employs Stochastic Subspace methods, proposed in [11]. Useful overviews of the proposed methods are given in [1,12,13] This paper proposes an application of a multitaper spectral estimator for mode frequency estimation.…”

Section: Introductionmentioning

confidence: 99%

Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations

Vanfretti

Bengtsson

Perić

et al. 2013

2013 IEEE Grenoble Conference

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Abstract-Spectral analysis applied to synchrophasor data can provide valuable information about lightly damped lowfrequency modes in power systems. This paper demonstrates application of two non-parametric spectral estimators focusing on mode frequency estimation. The first one is the well-known Welch spectral estimator whereas the application of Multitaper method is proposed here. In addition, the paper discusses mode estimator tuning procedures and the estimators' performances in the presence of "forced" oscillations. The validity of the proposed application of the non-parametric estimators and tuning procedures is verified through both simulated data and PMU data originating from the high-voltage grid of the Nordic power system. Special attention is given to the analysis of the behaviour of different low frequency modes present in the Nordic grid, including that of forced oscillations.

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Section: Introductionmentioning

confidence: 99%

Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations

Vanfretti

Bengtsson

Perić

et al. 2013

2013 IEEE Grenoble Conference

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“…It can identify modal parameters of linear structures from ambient structure vibrations [22]. The SSI method is recently applied to the identification of LFO modes.…”

Section: Comparison With Other Parameter Identification Methodsmentioning

confidence: 99%

A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform

Zhao

Nie

2016

Energies

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Abstract:To more completely extract useful features from low frequency oscillation (LFO) signals, a time-frequency analysis method using resonance-based sparse signal decomposition (RSSD) and a frequency slice wavelet transform (FSWT) is proposed. FSWT can cut time-frequency areas freely, so that any band component feature can be extracted. It can analyze multiple aspects of the LFO signal, including determination of dominant mode, mode seperation and extraction, and 3D map expression. Combined with the Hilbert transform,the parameters of the LFO mode components can be identified. Furthermore, the noise in the LFO signal could reduce the frequency resolution of FSWT analysis, which may impact the accuracy of oscillation mode identification. Complex signals can be separated by predictable Q-factors using RSSD. The RSSD method can do well in LFO signal denoising. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The LFO signal is the output of an underdamped system with high quality factor and high-resonance property at a specific frequency. The high-resonance component is the denoised LFO signal, and the residual contains most of the noise. Secondly, the high-resonance component is decomposed by FSWT and the full band of its time-frequency distribution are obtained. The 3D map expression and dominant mode of the LFO can be obtained. After that, due to its energy distribution, frequency slices are chosen to get accurate analysis of time-frequency features. Through reconstructing signals in characteristic frequency slices, separation and extraction of the LFO mode components is realized. Thirdly, high-accuracy detection for modal parameter identification is achieved by the Hilbert transform. Simulation and application examples prove the effectiveness of the proposed method.

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“…In [47], real-time monitoring of inter-area oscillations in the Nordic power system using PMUs is discussed. The use of stochastic subspace identification (SSI) for determining stability limits is demonstrated in [64]. Some of the benefits of SSI are small computational time, no disturbance is required to extract information from the measured data, and capability of dealing with signals containing noise.…”

Section: E Other Subspace Identification Methodsmentioning

confidence: 99%

Survey on power system stabilizers control and their prospective applications for power system damping using Synchrophasor‐based wide‐area systems

Chompoobutrgool

Vanfretti

Ghandhari

2011

Int Trans Elec Energy Syst

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Power system oscillation damping remains as one of the major concerns for secure and reliable operation of large power systems, and is of great current interest to both industry and academia. The principal reason for this is that the inception of poorly-damped low-frequency inter-area oscillations (LFIOs) when power systems are operating under stringent conditions may lead to systemwide breakups or considerably reduce the power transfers over critical corridors. With the availability of high-sampling rate phasor measurement units (PMUs), there is an increasing interest for effectively exploiting conventional damping control devices, such as power system stabilizers (PSSs), by using these measurements as control input signals. In this paper, we provide a comprehensive overview of distinct elements (or "building blocks") necessary for wide-area power system damping using synchrophasors and PSSs. These building blocks together shape a tentative methodical framework, and are disposed as follows: (1) fundamental understanding of the main characteristics of inter-area oscillations, (2) wide-area measurement and control systems (WAMS and WACS) and wide-area damping control (WADC), (3) advanced signal processing techniques for mode property identification, (4) methods for model-based small-signal analysis, (5) control input signals selection, and (6) methods for PSS control design. We also describe the latest developments in the implementation of synchrophasor measurements in WAMS and WACS as well as their prospectives for WADC applications. This paper serves both to abridge the state-of-the-art in each of these elements, and to accentuate aspiring ideas in each building block.

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Oscillatory Stability Limit Prediction Using Stochastic Subspace Identification

Cited by 114 publications

References 17 publications

Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations

Spectral estimation of low-frequency oscillations in the Nordic grid using ambient synchrophasor data under the presence of forced oscillations

A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform

Survey on power system stabilizers control and their prospective applications for power system damping using Synchrophasor‐based wide‐area systems

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