Generator dynamic estimation method based on robust volume Kalman filtering

Abstract

The invention discloses a generator dynamic estimation method based on robust cubature Kalman filtering. The generator dynamic estimation method is used for accurately estimating the generator dynamicstate under the condition that model parameters are uncertain. Firstly, a generator dynamic state estimation model is established; secondly, a robust cubature Kalman filter dynamic state estimator considering model parameter uncertainty is designed according to an uncertainty constraint criterion in a robust control theory in combination with cubature Kalman filtering; according to the method, the influence of model uncertainty on the state estimation precision can be inhibited, the dynamic state estimation precision of the generator under the condition that parameters are uncertain is improved, and relatively high robustness is achieved on model parameters. The method not only can effectively solve the problem of accurate estimation of the dynamic state of the generator under the condition of uncertain model parameters, but also is clear in process, simple to implement and convenient for engineering implementation.

Description

technical field [0001] The invention belongs to the technical field of power system analysis and monitoring, and particularly relates to a generator dynamic estimation method based on robust volumetric Kalman filtering. Background technique [0002] In order to obtain accurate power grid monitoring information, the synchrophasor measurement unit (PMU) of the Wide Area Measurement System (WAMS) has been gradually promoted and applied. state analysis. However, as a measurement system, WAMS will inevitably be affected by factors such as random interference during the measurement process, resulting in pollution of measurement data. Therefore, the measurement data obtained by the PMU cannot be directly used for the electromechanical transient analysis of the power system. Dynamic state estimation can not only effectively filter out errors and noise values ​​in measurement data, but also, with its prediction function, can formulate corresponding control strategies for possible f...

AI Technical Summary

Problems solved by technology

However, when the generator is running dynamically, the statistical characteristics of its system noise and measurement noise are difficult to obtain accurately. Not only that, some default constant parameters of the dynamic state estimation model of the generator will also be affected by the aging of the generator and the change of operating temperature. However, a certain degree of dynamic change occurs, that is, there will be uncertainty in the model parameters

These uncertain factors will seriously affect the performance of the dynamic state estimator and reduce the accuracy of state estimation.

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