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A Dynamic State Estimation Method of Generator Based on Unscented Transform Strong Tracking

A generator dynamic and state estimation technology, applied in the field of analysis and control, power system monitoring, can solve the problem of high dependence on prior knowledge of noise, and achieve the effect of high estimation accuracy

Active Publication Date: 2017-11-24
HOHAI UNIV
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Problems solved by technology

The unscented Kalman filter improves the filtering accuracy to the second order and above through the unscented transformation, but it needs to select a large number of parameters; the volumetric Kalman filter has high estimation accuracy and simple calculation, but it is highly dependent on the prior knowledge of noise

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  • A Dynamic State Estimation Method of Generator Based on Unscented Transform Strong Tracking
  • A Dynamic State Estimation Method of Generator Based on Unscented Transform Strong Tracking
  • A Dynamic State Estimation Method of Generator Based on Unscented Transform Strong Tracking

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Embodiment Construction

[0023] The technical process of the invention will be described in detail below in conjunction with the drawings:

[0024] 1 dynamic state estimation

[0025] Power system dynamic state estimation is based on Kalman filter theory to establish the framework of the entire algorithm. The research object of Kalman filter theory is a random dynamic process, using discrete measurement sequences, with the goal of minimizing the filter covariance, and finally obtaining the optimal estimation value of the discrete state sequence. Dynamic state estimation is generally divided into prediction step and filtering step:

[0026] Prediction step:

[0027]

[0028] Where the superscript T represents the transposition of the matrix, the subscript k represents the moment k, and k+1|k represents the prediction of the moment k at the moment k+1, Is the predicted value of the system state variable at k+1, F k Is the state transition matrix at time k, Is the filtered value of the system state variable a...

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Abstract

The invention discloses a generator dynamic state estimation method based on unscented transformation and strong tracking filtering. The method is divided into two steps for generator dynamic state estimation, namely a prediction step and a filtering step. The mean and filter covariance matrix adopts a symmetric sampling strategy for sigma point sampling, calculates the measurement prediction calculation value, obtains the residual equation, and introduces the fading factor to modify the prediction covariance matrix; the filter step adjusts the gain matrix online, and after the correction, the electromechanical transient is obtained. The estimated value of the generator power angle and electrical angular velocity during the state process. Compared with the unscented Kalman filter and the strong tracking filter, the method for estimating the dynamic state of the generator based on the unscented transform and strong tracking filter of the present invention is improved in terms of tracking speed, accuracy and robustness to noise.

Description

Technical field [0001] The invention relates to a generator dynamic state estimation method based on strong tracking without trace conversion, which belongs to the technical field of power system monitoring, analysis and control. Background technique [0002] Power system state estimation is mainly divided into static state estimation and dynamic state estimation. In recent years, a phasor measurement unit (PMU) based on a wide-area measurement system has made it possible to accurately track the electromechanical transient state of the power system. However, due to the existence of measurement errors, direct electromechanical transient analysis using the raw data measured by the PMU cannot obtain accurate results, which will ultimately affect the effective and real-time monitoring of the system and the formulation of corresponding stable control strategies. Dynamic state estimation can not only filter out errors and noise in the measurement data, but its predictive ability can a...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/34
Inventor 孙国强黄蔓云卫志农孙永辉臧海祥厉超
Owner HOHAI UNIV
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