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Dynamic state estimation method based on adaptive EnKF technology

A technology of dynamic state estimation and state estimation, applied in calculation, computer-aided design, design optimization/simulation, etc., can solve the problem of reducing the accuracy of state estimation, the mismatch between the system noise covariance matrix value and the real value, and affecting the dynamic state estimation results and other issues to achieve accurate estimation and reduce the impact

Pending Publication Date: 2019-07-12
HOHAI UNIV
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Problems solved by technology

However, it is worth noting that these methods all assume that the variance of system noise is constant; in actual power systems, the statistical characteristics of system noise are difficult to obtain accurately and change dynamically
Therefore, assuming that the system noise covariance matrix is ​​constant, the set system noise covariance matrix value will not match the real value, which will seriously affect the dynamic state estimation results and reduce the state estimation accuracy.

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  • Dynamic state estimation method based on adaptive EnKF technology
  • Dynamic state estimation method based on adaptive EnKF technology
  • Dynamic state estimation method based on adaptive EnKF technology

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Embodiment

[0078] (a) Model building

[0079] According to the fourth-order dynamic equation of the generator, the state estimation equation of the generator is constructed as follows:

[0080]

[0081] 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 a self-adaptive EnKF technology, which is used for dynamic state estimation of a generator of a power system under an unknown systemnoise working condition. According to the invention, an improved fading memory index weighted Sage-Husa noise statistical estimator is introduced into the EnKF, which can dynamically estimate and correct the mean value and the variance of time-varying system noise and inhibit the influence of unknown system noise on the state estimation precision, so that the accurate estimation of the generator operation state is achieved. Due to the fact that the actual engineering background is considered, the method 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 self-adaptive EnKF technology. Background technique [0002] State estimation is also called filtering, which uses the redundancy of the real-time measurement system to improve data accuracy, remove random noise in the measured values, and realize accurate monitoring of the power system state. In general, power system state estimation can be divided into two categories, namely, static state estimation and dynamic state estimation. Among them, the application of static state estimation is relatively mature at present, and it can obtain the state information of the system during steady state operation. However, the static state estimation ignores the dynamic characteristics of the system, and cannot realize the online monitoring of the system state. In order to make up for the shortcomings of static state est...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06F17/16G06F17/11
CPCG06F17/11G06F17/16G06F30/20
Inventor 孙永辉王义侯栋宸王森熊俊杰曹阳吕欣欣
Owner HOHAI UNIV
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