Analysis of Low Frequency Oscillations using improved Hilbert-Huang Transform

Yang, Dong; Rehtanz, Christian; Li, Y.

doi:10.1109/powercon.2010.5666567

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…Moreover, the HHT method can determine the contribution of a state variable in a mode based on energy and phase relationship of IMFs. Nevertheless, as stated in the introduction, the HHT method suffers from the EEs, mode-mixing and Gibbs phenomena [6,9]. In this paper, an improved Prony method is proposed that is able to calculate IMFs and thus NCFs for a given signal.…”

Section: Motivation Behind Methodology Selectionmentioning

confidence: 99%

“…Recently, several studies have attempted to extract oscillation parameters using synchrophasor data. Some of the well-known methods used for oscillation detection using measurement-based approaches include: Kalman Filter (KF) [4] estimation of signal parameters by rotational invariance technique (ESPRIT) [5], Hilbert-Huang transform (HHT) [6][7][8][9], Yule-Walker algorithm [10], wavelet transform (WT) [11], recursive adaptive stochastic subspace identification (RASSI) [12], maximum likelihood method [13], and Prony analysis method [14][15][16]. Among them, the Prony method is one of the most commonly used measurement-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…However, EMD process has some inherent shortcomings such as the end effects (EEs), mode-mixing and Gibbs phenomena. Some of those drawbacks have been addressed in improved versions of the HHT method in [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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An Augmented Prony Method for Power System Oscillation Analysis Using Synchrophasor Data

Arpanahi

Kordi²,

Torkzadeh

et al. 2019

Energies

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Intrinsic mode functions (IMFs) provide an intuitive representation of the oscillatory modes and are mainly calculated using Hilbert–Huang transform (HHT) methods. Those methods, however, suffer from the end effects, mode-mixing and Gibbs phenomena since they use an iterative procedure. This paper proposes an augmented Prony method for power system oscillation analysis using synchrophasor data obtained from a wide-area measurement system (WAMS). In the proposed method, in addition to the estimation of the modal information, IMFs are extracted using a new explicit mathematical formulation. Further, an indicator based on an energy and phase relationship of IMFs is proposed, which allows system operators to recognize the most effective generators/actuators on specific modes. The method is employed as an online oscillation-monitoring framework providing inputs for the so-called wide-area damping control (WADC) module. The efficacy of the proposed method is validated using three test cases, in which the IMFs calculation is simpler and more accurate if compared with other methods.

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Section: Motivation Behind Methodology Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Augmented Prony Method for Power System Oscillation Analysis Using Synchrophasor Data

Arpanahi

Kordi²,

Torkzadeh

et al. 2019

Energies

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“…In ref. [16], four different kinds of extension methods, which are based on the relationships of extrema, were compared with each other. It has been proved that the symmetrical extrema extension (SEE) has better performance over the envelop extrema extension, local wave-matching extension and linear envelop extension in analyzing of LFO problem.…”

Section: Empirical Mode Decomposition With Symmetrical Extrema Extensmentioning

confidence: 99%

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

Yang

Rehtanz

et al. 2011

Sci. China Technol. Sci.

Self Cite

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In this paper, a novel method that integrates the improved empirical mode decomposition (EMD) and signal energy algorithm is proposed to estimate the dominant oscillation parameters and corresponding mode shape. Firstly, the EMD with symmetrical extrema extension (SEE) is utilized to decompose the measured data from wide area measurement system (WAMS) into a finite set of intrinsic mode functions (IMFs). Then, the signal energy algorithm is used to calculate the approximate oscillation parameters of the IMFs. The nodes involved the dominant oscillation mode are classified based on the calculated frequency and reasonable threshold. Furthermore, for the dominant oscillation mode, the IMF with maximum mean amplitude is defined as the reference. Next, the relative phases (RPs) between the reference IMF and other IMFs are calculated in order to identify the negative and positive oscillation groups. According to the values of RPs, the coherent group and corresponding node contribution factor (NCF) can be identified, and the dominant approximate mode shape (AMS) can also be determined. The efficiency of the proposed approach is tested by applying it to synthetic signal and measured data from the simulation model. low frequency oscillation, wide area measurement system, symmetrical extrema extension, signal energy algorithm, total oscillation energy, node oscillation energy, node contribution factor Citation:Yang D C, Rehtanz C, Li Y, et al. A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm.

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“…The improved Prony method identify accurately dominant oscillation mode and get detailed parameter of lower frequency oscillation, however, the improved method had also deficiency, included fail simulated with noise and non-stationary signal, made sure difficultly the order of decomposed signal and took longer time to calculate [4][5]. A improved HHT for Online Identification of Low Frequency Oscillation Modes of Power System was proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

A new approach for online identification of low frequency oscillation modes of power system based on the improved Hilbert-Huang transform

Yang

Kang

Yue

2014

2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

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A new nonlinearity, instability and non-stationary signal processing method named improved Hilbert-Huang transform was proposed to analyze the measured oscillatory signal from wide area measurement system. Based on this method, the mode mixing of the measured signal in decomposition process was removed and the scheme has advantages of good performance of anti-interference. The measured signal was decomposed into a series of models and identified the amplitude, frequency and damping of lowfrequency oscillation signal. The measured signals from simulation model and actual power system indicate the validity and feasibility of the proposed method.KEY WORDS：Hilbert-Huang Transform (HHT); lowfrequency oscillation model; power system; online identification I.

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Analysis of Low Frequency Oscillations using improved Hilbert-Huang Transform

Cited by 15 publications

References 7 publications

An Augmented Prony Method for Power System Oscillation Analysis Using Synchrophasor Data

An Augmented Prony Method for Power System Oscillation Analysis Using Synchrophasor Data

A novel method for analyzing dominant oscillation mode based on improved EMD and signal energy algorithm

A new approach for online identification of low frequency oscillation modes of power system based on the improved Hilbert-Huang transform

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