Measurement-based solution for low frequency oscillation analysis

Shen, Chen; An, Zhi; Dai, Xianzhong; Wei, Wei; Liu, Ding

doi:10.1007/s40565-016-0200-0

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…4) The maximum amplitude of each frequency band is compared with A th . FFT exhibits the fence effect, which can potentially lower the effective component altitudes to less than the threshold and which in turn requires that the maximum amplitude, e. g., the interpolation FFT method [17], should be corrected. The corrected amplitudes are compared with A th to determine whether they are effective components.…”

Section: ) Process Of Oscillation Identificationmentioning

confidence: 99%

“…The existing oscillation/component signal identification methods mainly include fast Fourier transform (FFT) and its variants [14], Prony [15], [16], EMD [17], TLS-ESPRIT [18], and Taylor Fourier transform [19]. These methods focus on the extraction accuracy of the oscillation components while attempting to determine whether a frequency component is the random noise or an effective component is limited.…”

Section: Introductionmentioning

confidence: 99%

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Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Líu

2023

Journal of Modern Power Systems and Clean Energy

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Synchrophasor measurement units (PMUs) provide synchronized measurement data for wide-area applications. To improve the effectiveness of synchrophasor-based applications, field PMUs must be tested to ensure their performance and data quality. In the companion paper (Part I), we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center. Part I presents the development and test of the PMU calibrator. This paper focuses on the analysis center and field test applications. First, the critical component of the analysis center is the signal identification module, for which the step and oscillation signal identification methods are proposed. Here, the performance evaluation criteria of PMU in these two cases are different from others. The methods include a step signal detection method based on singular value decomposition (SVD), which has the capability of weak step detection to account for energy leakage of the signal during the step process, and an oscillation signal identification method based on SVD and fast Fourier transform, which can accurately extract oscillation components that benefit from the adaptive threshold setting method. Second, the analysis center software is implemented based on identification results. By integrating the PMU calibrator in Part I with the analysis center in Part II, we can examine in depth the field PMU test applications in three test scenarios, including standard, playback, and field signal test. Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

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Section: ) Process Of Oscillation Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Líu

2023

Journal of Modern Power Systems and Clean Energy

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“…The inequality and equality constraints defined for the specification of the optimal parameters can be written using (7) and (8). As a result, the equality constraints are:…”

Section: Controller Designmentioning

confidence: 99%

“…There are two categories for the classification of typical data, including ambient and ring-down data. Ambient data are aggregated from a power system under stable conditions without any major disruption, and the perturbation results from load changes [8]. After a significant disturbance such as generator or load tripping, the ring-down data emerge as observable oscillations in the system variables.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

Faraji

Naghshbandy

Baayeh

2021

Journal of Modern Power Systems and Clean Energy

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The occurrence of low-frequency electromechanical oscillations is a major problem in the effective operation of power systems. The scrutiny of these oscillations provides substantial information about power system stability and security. In this paper, a new method is introduced based on a combination of synchrosqueezed wavelet transform and the stochastic subspace identification (SSI) algorithm to investigate the low-frequency electromechanical oscillations of large-scale power systems. Then, the estimated modes of the power system are used for the design of the power system stabilizer and the flexible alternating current transmission system (FACTS) device. In this optimization problem, the control parameters are set using a hybrid approach composed of the Prony and residual methods and the modified fruit fly optimization algorithm. The proposed mode estimation method and the controller design are simulated in MATLAB using two test case systems, namely IEEE 2-area 4-generator and New England-New York 68-bus 16-generator systems. The simulation results demonstrate the high performance of the proposed method in estimation of local and interarea modes, and indicate the improvements in oscillation damping and power system stability.

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“…The measured synchrophasor data can be used for both real-time applications such as dynamic monitoring, oscillation source location, smart control, and wide-area protection [1][2][3][4][5], and off-line applications such as post-event analysis [6]. With the rapid development of interconnected power systems, renewable energy sources, and high-voltage direct current (HVDC) transmission systems, oscillation problems such as lowfrequency oscillations (LFOs) and subsynchronous oscillations (SSOs) are drawing more and more attention [7][8][9][10], which may threaten power system security and stability. WAMS can be used for solving these problems.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based data compression for wide-area measurement data of oscillations

Cheng

Zhang

et al. 2018

J. Mod. Power Syst. Clean Energy

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This paper proposes a wavelet-based data compression method to compress the recorded data of oscillations in power systems for wide-area measurement systems. Actual recorded oscillations and simulated oscillations are compressed and reconstructed by the waveletbased data compression method to select the best wavelet functions and decomposition scales according to the criterion of the minimum compression distortion composite index, for a balanced consideration of compression performance and reconstruction accuracy. Based on the selections, the relationship between the oscillation frequency and the corresponding optimal wavelet and scale is discussed, and a piecewise linear model of the base-2 logarithm of the frequency and the order of the wavelet is developed, in which different pieces represent different scales. As a result, the wavelet function and decomposition scale can be selected according to the oscillation frequency. Compared with the wavelet-based data compression method with a fixed wavelet scale for disturbance signals and the real-time data compression method based on exception compression and swing door trending for oscillations, the proposed method can provide high compression ratios and low distortion rates.

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Measurement-based solution for low frequency oscillation analysis

Cited by 9 publications

References 19 publications

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

Field PMU Test and Calibration Method—PartII: Test Signal Identification Methods and FieldTest Applications

A Hybrid Coordinated Design Method for Power System Stabilizer and FACTS Device Based on Synchrosqueezed Wavelet Transform and Stochastic Subspace Identification

Wavelet-based data compression for wide-area measurement data of oscillations

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