Identification of Dominant Low Frequency Oscillation Modes Based on Blind Source Separation

Zhang, A. Q.; Zhang, L. L.; Li, M. S.

doi:10.1109/tpwrs.2017.2685533

Cited by 26 publications

(18 citation statements)

References 28 publications

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“…However, this method is only suitable for off-line analysis which depends on the accuracy of the model and parameters. Therefore, it is not suitable for the analysis of large-scale and high-order systems [6], [7]. The other method is based on real-time measuring information which has developed with the promotion and application of wide-area measurement systems (WAMS) in the past decade [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

Chen

Mohamed

et al. 2020

IEEE Access

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One of the main reasons that affecting the stability of power systems is low frequency oscillation (LFO). The existence of noise influences the accuracy of LFO mode identification extracted from wide-area measurement system (WAMS). The wavelet threshold de-noising is widely used in signal processing. In this paper, wavelet soft threshold is illustrated to attenuate the noise of LFO signal, the optimal wavelet basis and decomposition level for de-noising LFO signal with noise are obtained and verified by experiments. Following the signal de-noising, an improved Matrix Pencil (MP) algorithm is used for mode identification of LFO. This improvement particularly lies in the ratio of adjacent singular entropy increment difference (RASEID) designed as an adaptive order determination method in the MP algorithm proposed in the paper. RASEID not only makes the MP algorithm adaptive, but also enhances the stability of the order determination in the mode identification process. The proposed method ensures the accuracy of mode identification with lower sensitivity to noise interference. Finally, the validity of the proposed method is verified by three cases studies. The first study is on the analysis of synthetic signal typically performed in many literatures. The second study is to identify the mode of active power LFO signal which is generated by IEEE fourgenerator and two-area system given with disturbance on RT-LAB experimental platform. The third study is the oscillation analysis of the actual LFO data in the North American power grid. The results validate the feasibility of the proposed method for mode identification of noisy LFO. INDEX TERMS Low-frequency oscillation (LFO), wavelet soft-threshold de-noising, ratio of adjacent singular entropy increment difference (RASEID), adaptive, Matrix Pencil (MP), RT-LAB platform.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

Chen

Mohamed

et al. 2020

IEEE Access

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“…Widespread used renewable and sustainable energy sources bring modern power systems electromechanical oscillations, synchronous, and subsynchronous oscillations [1][2][3][4][5][6]. The low-frequency oscillations (LFO) may result in unstable and unsecure operation of power systems, so the real-time identification of frequency, amplitude, and damping factor are still essential in recent years [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Robust Direct Parameter Identification of Exponentially Damped Low-Frequency Oscillation in Power Systems

et al. 2020

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Low-frequency oscillations in power systems can be modeled as an exponentially damped sinusoid (EDS) signal. Its frequency, damping factor, and amplitude are identified by the robust algorithm proposed in this paper. Under the condition of no noise, the exponentially convergent property of the proposed identification is proved by the use of time scale change, variable transformation, slow integral manifold, averaging method, and Lyapunov stability theorem in sequence. Under the condition of bounded additive noise, the antinoise performance of the identification of each parameter is investigated by the perturbed system theorem and error synthesis principle. The robustness of the proposed method is embodied in the following aspects: the exponential convergence for EDS signal with a wide range of frequency, especially with a rather low frequency; the boundary values of identification errors resulting from high-frequency sinusoidal noise of both frequency and damping factor can be adjusted by tuning the design parameters; and the different effects of the four design parameters on tracking performance and antinoise performance of each parameter identification. Simulation results demonstrate the performance of the algorithm and validate the conclusions.

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“…Signal processing and spectral decomposition methods play a key role for the oscillation estimation methods in power systems based on measurements. The main methods applied to this topic include Hankel Singular Value Decomposition (HSVD) [7], Hilbert Spectral Analysis [8], the Extended Complex Kalman Filter (ECKF) [9], and wavelet-based method [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Impact of non-synchronous generation on transmission oscillations paths

Chamorro

Ordóñez

Peng

et al. 2018

2018 IEEE Texas Power and Energy Conference (TPEC)

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The large scale penetration of non-synchronous generation has been causing several impacts on the power systems dynamics. The low-frequency oscillations affect the power exchanged along the transmission lines/corridors. This paper uses the Multi-Prony Analysis mode estimation technique to monitor and suggest the dominant oscillation modes which can be useful for wide-area control purposes. Moreover, the oscillation modes are also monitored under gradual cases of non-synchronous generation integration in the system. The methodology is applied to two different test transmission systems: i) the two area system and, ii) the Nordic 32 system. The results illustrate the similarity and differences in the scenarios proposed.

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Identification of Dominant Low Frequency Oscillation Modes Based on Blind Source Separation

Cited by 26 publications

References 28 publications

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

A Robust Direct Parameter Identification of Exponentially Damped Low-Frequency Oscillation in Power Systems

Impact of non-synchronous generation on transmission oscillations paths

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