Mode shape estimation algorithms under ambient conditions: A comparative review

Dosiek, Luke; Zhou, N.; Pierre, John W.; Huang, Zhenyu; Trudnowski, Daniel

doi:10.1109/tpwrs.2012.2210570

Cited by 84 publications

(41 citation statements)

References 20 publications

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“…To utilize the mode shape information in real systems, many measurementbased methods for estimating the mode shape have been proposed. An overview on existing estimation techniques using either ring-down signals or ambient signals can be found in [34] and its references.…”

Section: Mode Shape Estimation Based Methodsmentioning

confidence: 99%

Location methods of oscillation sources in power systems: a survey

Wang

Sun

2016

J. Mod. Power Syst. Clean Energy

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The deployment of a synchrophasor-based widearea measurement system (WAMS) in a power grid largely improves the observability of power system dynamics and the operator's real-time situational awareness for potential stability issues. The WAMS in many power grids has successfully captured system oscillation events, e.g. poorly damped natural oscillations and forced oscillations, from time to time. To identify the root cause of an observed oscillation event for further mitigation actions, many methods have been proposed to locate the source of oscillation based on different ideas and principles. However, most methods proposed so far for locating the oscillation source in a power grid are not reliable enough for practical applications. This paper presents a comprehensive review of existing location methods, which basically fall into four major categories, plus a few other methods. Their advantages and disadvantages are discussed in detail. Some trends and challenges on the problem of oscillation source location are pointed out along with potential future research directions. Finally, a practical, general scheme for oscillation source location using available location methods is suggested and analyzed.

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Section: Mode Shape Estimation Based Methodsmentioning

confidence: 99%

Location methods of oscillation sources in power systems: a survey

Wang

Sun

2016

J. Mod. Power Syst. Clean Energy

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“…Subspace methods can also give accurate estimates of mode shapes giving more information about the oscillation modes like the dominant oscillation paths and the source. This is due to the fact that there is no approximation when mode shapes are calculated [10].…”

Section: Mode Estimation Via Subspace Identification Methodsmentioning

confidence: 99%

“…Electromechanical oscillation modes can thus be estimated from this model. In this paper, since only ambient data are used, i.e., the system is perturbed by small load variations, the identified linearized power system via subspace identification methods can be written as [10] follows:…”

Section: Modal Properties Estimation Via Subspace Identificationmentioning

confidence: 99%

“…Subspace methods are also developed to estimate mode shapes. It is concluded that the mode shapes obtained by subspace methods are more accurate compared with the ones obtained by other methods such as transfer function (TF), spectral frequency domain decomposition (FDD) [10]. Furthermore, no extra algorithm is required to calculate the mode shapes if the subspace method is applied.…”

Section: Introductionmentioning

confidence: 96%

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Improvements for electromechanical oscillation mode estimation via subspace identification methods

Ding

Huang

2015

EURASIP J. Adv. Signal Process.

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Two improvements of power system electromechanical oscillation estimation via subspace identification are proposed in this paper. Singular value-based criteria are introduced not only to improve the computation efficiency for determining the model order of the estimated system but also to improve the accuracy of the estimates. A mode matching method based on the characteristics of accurate mode shape estimates is proposed to ensure the correctness of the estimates. Numerical results from three different scale power systems illustrate the feasibility of the proposed methods.

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“…They may limit the power output of generators and transmission capability of tie lines, and in some serious circumstances may cause the system to collapse [1][2][3][4][5][6]. The LFO signal is a typical non-stationary signal, which demonstrates non-stationary signal characteristics such as an amplitude that changes over time and the random emergence of excitation modes, so it is not appropriate that its oscillation character is presented in the time domain or in the frequency domain alone.…”

Section: Introductionmentioning

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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Mode shape estimation algorithms under ambient conditions: A comparative review

Cited by 84 publications

References 20 publications

Location methods of oscillation sources in power systems: a survey

Location methods of oscillation sources in power systems: a survey

Improvements for electromechanical oscillation mode estimation via subspace identification methods

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

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