An Adaptive Strong Tracking Unscented Kalman Filter Synchrophasor Parameter Estimation Method

Abstract

The present invention proposes an adaptive strong tracking unscented Kalman filter synchrophasor parameter estimation method, which constructs a decision variable by analyzing the characteristics of the residual sequence in the two states of state variable constant and sudden change, and uses the decision variable to judge the state Whether there is a sudden change in the variable, and then adaptively change the value of the weakening factor, so as to reduce the value of the weakening factor as much as possible on the basis of ensuring the estimation error of the residual sequence covariance is shielded, thereby speeding up the tracking of the strong tracking unscented Kalman filter algorithm The speed of state variable mutation improves the accuracy of phasor measurement.

Description

technical field [0001] The invention belongs to the field of wide-area synchronized phasor measurement, in particular to an adaptive strong tracking unscented Kalman filter synchronized phasor parameter estimation method. Background technique [0002] Due to the advantages of real-time and measurement accuracy, the Kalman filter method has gradually attracted the attention of scholars at home and abroad in recent years. The synchrophasor measurement method using this method must first establish a state-space model of the power signal, and then use the sequentially obtained sampling points The state prediction and filter update are carried out on the state variables of the power signal and the quantity measurement, so as to achieve the purpose of sequentially estimating the state variables of the power signal and gradually approaching the true value. Since the function of the power signal and its frequency must be nonlinear, the classical Kalman filter method, which can only ...

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Problems solved by technology

In a high-noise environment, the estimation accuracy of residual sequence covariance decreases sharply, which requires setting a larger attenuation factor to shield the larger estimation error, but an excessive attenuation factor will cause the method to track mutations slowly or fail to track mutations , a too small attenuation factor will cause the method to diverge due to the inability to shield the estimation error of the residual sequence

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