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Dynamic state estimation method based on self-adaptive volume Kalman filtering

A technology of dynamic state estimation and Kalman filtering, applied in computing, data processing applications, motor generator testing, etc., can solve problems such as difficult to obtain accurate statistical characteristics of system noise, inability of state estimators to converge, and reduced accuracy of state estimation

Pending Publication Date: 2019-07-19
HOHAI UNIV
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Problems solved by technology

However, it should be pointed out that these methods all assume that the covariance matrix that the system noise satisfies is a constant; in the actual power system, the statistical characteristics of the system noise are difficult to obtain accurately and change dynamically, and the setting of the system noise covariance matrix is closely related to the performance of the state estimator
Therefore, if the system noise covariance matrix setting deviates from its true value, it will seriously reduce the accuracy of state estimation, and even cause the state estimator to fail to converge to the true value

Method used

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  • Dynamic state estimation method based on self-adaptive volume Kalman filtering
  • Dynamic state estimation method based on self-adaptive volume Kalman filtering
  • Dynamic state estimation method based on self-adaptive volume Kalman filtering

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Embodiment

[0102] (a) Model building

[0103] According to the fourth-order dynamic equation of the generator, the state estimation equation of the generator is constructed as follows:

[0104]

[0105] In the formula: δ represents generator power angle, rad; ω and ω 0 Respectively, electrical angular velocity and synchronous rotational speed, pu; e' q and e' d respectively represent the transient electromotive force of the generator q-axis and d-axis; H represents the inertia constant of the generator, T m and T e represent the mechanical power and electromagnetic power of the generator, respectively, where T e =P e / ω;K D Indicates the damping factor, E fd is the stator excitation voltage; T′ d0 and T' q0 Indicates the open-circuit time constant of the generator in the d-q coordinate system; x d and x' d Respectively represent the d-axis synchronous reactance and transient reactance of the generator, x q and x' q are the generator q-axis synchronous reactance and transi...

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Abstract

The invention discloses a dynamic state estimation method based on self-adaptive volume Kalman filtering, which is used for realizing accurate estimation of a dynamic state variable of an electric power system generator. According to the method, a fading memory index weighted Sage- Husa noise statistical estimator is introduced into volume Kalman filtering; and the mean value and the variance of the time-varying system noise are dynamically estimated and corrected, the influence of system noise matrix mismatching on the state estimation precision is inhibited, and the accurate estimation of the state variable of the generator is improved. The algorithm considers the actual engineering background, is simple and convenient, and has high engineering application value.

Description

technical field [0001] The invention belongs to the technical field of power system analysis and monitoring, in particular to a dynamic state estimation method based on an adaptive volumetric Kalman filter. Background technique [0002] Accurate state estimation is of great significance for power system analysis and stability control. State estimation is generally divided into two categories, one is static estimation, and the other is dynamic state estimation. The static state estimation utilizes the redundant measurement information of the section at a certain time to realize the state variable estimation of the system at that time. Although the static state estimation accuracy is high, it ignores the dynamic characteristics of the power system. Therefore, static state estimation cannot be applied to real-time online estimation of power system state. In order to meet the needs of online monitoring of power systems, dynamic state estimation methods with estimation and pre...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06F17/16G01R31/34G06Q50/06
CPCG06F17/16G01R31/343G06Q50/06G06F30/20
Inventor 孙永辉王义胡银龙王森侯栋宸王朋吕欣欣翟苏巍
Owner HOHAI UNIV
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