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Power generator dynamic state estimation method based on unscented transformation strong tracking filtering

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: 2015-07-15
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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  • Power generator dynamic state estimation method based on unscented transformation strong tracking filtering
  • Power generator dynamic state estimation method based on unscented transformation strong tracking filtering
  • Power generator dynamic state estimation method based on unscented transformation strong tracking filtering

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Embodiment Construction

[0023] Below in conjunction with accompanying drawing, the technical process of invention is described in detail:

[0024] 1 Dynamic state estimation

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

[0026] Prediction step:

[0027]

[0028] In the formula, the superscript T represents the transposition of the matrix, the subscript k represents time k, and k+1|k represents the prediction of time k to time k+1, is the predicted value of the system state variable at time k+1, F k is the state transition matrix at time k, is the filtered value of the s...

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Abstract

The invention discloses a power generator dynamic state estimation method based on unscented transformation strong tracking filtering. The power generator dynamic state estimation method comprises two steps, namely, prediction and filtering, wherein in the prediction step, sigma point sampling is carried out through the symmetrical sampling strategy according to a filtering mean value and a filtering covariance matrix at a previous moment, a predicted measurement calculation value is calculated, a residual equation is obtained, and a fading factor is introduced to correct a predicted covariance matrix; in the filtering step, a gain matrix is adjusted in online mode, and estimate values of the power angle and electrical angle speed of a power generator in the electromechanical transient process are obtained after correction. Compared with unscented Kalman filtering and strong tracking filtering, the power generator dynamic state estimation method provided by the invention is high in tracking speed, tracking precision and noise robustness.

Description

technical field [0001] The invention relates to a generator dynamic state estimation method based on unscented transformation strong tracking, 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, synchrophasor measurement units (PMUs) based on wide-area measurement systems have made it possible to accurately track electromechanical transients in power systems. However, due to the existence of measurement errors, it is impossible to obtain accurate results by directly using the raw data measured by the PMU for electromechanical transient analysis, which ultimately affects the effective and real-time monitoring of the system and the formulation of corresponding stability control strategies. Dynamic state estimation can not only filter out errors and noises in measurement data, but its predic...

Claims

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Application Information

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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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