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Low frequency oscillation online identification method based on cross-correlation function denoising algorithm

A cross-correlation function and low-frequency oscillation technology, which is applied in the field of electrical information, can solve problems that affect the accuracy of low-frequency oscillation mode identification, limited noise processing capabilities, and insufficient colored noise.

Inactive Publication Date: 2014-07-23
SICHUAN UNIV +3
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AI Technical Summary

Problems solved by technology

The existence of Gaussian colored noise will produce estimation deviation, thus affecting the accuracy of low frequency oscillation mode identification
At present, power system modal identification generally uses its own singular value decomposition to eliminate noise. In this case, the estimation of colored noise is not enough, especially for the low signal-to-noise ratio. The noise processing ability is limited

Method used

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  • Low frequency oscillation online identification method based on cross-correlation function denoising algorithm
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  • Low frequency oscillation online identification method based on cross-correlation function denoising algorithm

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Embodiment

[0047] The four-machine two-area system is selected as a power system simulation example, and the disturbance is set as a three-phase short circuit of AC line AC5, which lasts for 0.05s. Take the power oscillation signal of the AC connection line (AC7) between the two areas of the system as the identification signal.

[0048] Firstly, the AC7 power signal of the AC tie line is identified without noise. In order to compare the performance of the CCF and FOMMC algorithms, the TLS-ESPRIT, FOMMC-TLS-ESPRIT, and CCF-TLS-ESPRIT algorithms are used for identification respectively, and the mode with the largest amplitude is taken. state, as shown in Table 1.

[0049] Table 1 Identification results without adding noise

[0050]

[0051]

[0052] It can be known from Table 1 that this system has a weakly damped interval oscillation main vibration mode, in addition to this, there is a 0.96Hz weakly damped non-main vibration mode. In the absence of noise interference, there is lit...

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Abstract

The invention discloses a low frequency oscillation online identification method based on a cross-correlation function denoising algorithm. The low frequency oscillation online identification method based on the cross-correlation function denoising algorithm is characterized in that cross-correlation functions are used for replacing actual measured signals to restrain color noise, and modal identification of low frequency oscillation is carried out by combining a total least square-rotation invariant technology parameter estimation (TLS-ESPRIT) algorithm. The algorithm can identify the modality of the low frequency oscillation of a power system under a color noise environment quickly and accurately, and an effective basis is provided for security and stability analysis and restraining measures of a system.

Description

technical field [0001] The invention relates to an on-line identification method of low-frequency oscillation based on a cross-correlation function noise filtering algorithm. Specifically, the invention replaces the measured signal with a cross-correlation function, thereby suppressing color noise, and combining overall least squares-rotation invariant technology Parameter estimation (TLS-ESPRIT) algorithm is used for mode identification of low-frequency oscillation, which belongs to the field of electrical information. Background technique [0002] With the interconnection of national power grids, low-frequency oscillations between regions will directly affect the power exchange between regions and restrict the transmission capacity of tie lines. The traditional low-frequency oscillation modal analysis method has a large amount of calculation and is only suitable for offline analysis. With the widespread application of Wide Area Measurement System (WAMS), especially Phasor...

Claims

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Application Information

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IPC IPC(8): H02J3/24G01R31/00
Inventor 李兴源胡楠王峰刘天琪陈实曾琦
Owner SICHUAN UNIV
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