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Low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering

An extended Kalman and low-frequency oscillation technology, which is applied in the field of power systems, can solve the problems of the influence of identification results and the inability to consider the uncertainty of oscillation signals, etc., and achieve strong robustness and avoid parameter identification errors.

Inactive Publication Date: 2018-03-16
HOHAI UNIV
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Problems solved by technology

However, it should be noted that the EKF method cannot consider the uncertainty introduced in the process of modeling the oscillatory signal, and its identification results are easily affected by the noise initial variance matrix

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  • Low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering
  • Low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering
  • Low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering

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Embodiment

[0067] Embodiment: In order to verify the effectiveness and practicability of the method of the present invention, this embodiment selects the following low-frequency oscillation signals of the power system for parameter identification analysis

[0068] y(t)=e -δt Cos(wt+φ)+n(t)

[0069] The low frequency oscillation signal is composed of an exponentially decaying sinusoidal signal. The parameters of the low-frequency oscillation signal to be identified: damping factor δ=0.01, frequency w=0.5rad / s, φ=0, n(t) is Gaussian white noise, and the covariance matrix satisfied by it is r=10 -5 , the sampling time is taken as T=1s. In this embodiment, the measured values ​​at the first 300 sampling times are used for algorithm verification during the simulation experiment, that is, N is 300.

[0070] When using the method proposed by the present invention to identify the parameters of the low-frequency oscillation signal of the embodiment, the initial values ​​of the filter initial es...

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Abstract

The invention provides a low-frequency oscillation signal parameter identification method based on H infinite extended Kalman filtering. During low-frequency oscillation signal parameter identification based on an H infinite filtering theory, the influence on model uncertainty is effectively considered, and parameter identification errors caused by model parameter uncertainty are avoided. Due to the fact that a noise covariance matrix self-adaptive technique is adopted, a covariance matrix is dynamically adjusted, accordingly the proposed method has stronger robustness, and obtaining of more accurate low-frequency oscillation signal parameter identification results is facilitated.

Description

technical field [0001] The invention relates to an electric power system, in particular to a method for extracting a low-frequency oscillation signal of the electric power system. Background technique [0002] In recent years, in the process of national power grid interconnection and west-to-east power transmission, power exchanges have become more frequent, and most of the security and stability problems of power systems are manifested as low-frequency oscillations. Therefore, how to effectively extract the information represented by the low-frequency oscillation signal of the power system is of great significance for the security and stability analysis of the power system. [0003] In the current research, it is an effective way to study the low frequency oscillation of the power system by using the field measured data to analyze and process the signal to obtain the characteristic parameters of the oscillation. The commonly used methods mainly include real-time fast Fouri...

Claims

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Application Information

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IPC IPC(8): G01R23/16G06F17/16
CPCG01R23/16G06F17/16
Inventor 王义钟永洁孙永辉武小鹏吕欣欣翟苏巍
Owner HOHAI UNIV
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