A Generator Dynamic Estimation Method Considering Model Parameter Uncertainty

Abstract

The invention provides a generator dynamic estimation method considering model parameter uncertainty. According to the method, a generator dynamic state estimation model is established, wherein a classic model of a generator is established, and a dynamic state estimation equation and a measurement equation of the generator are constructed; error analysis is performed, wherein an error variance matrix of measured values and process noise are considered; and adaptive robust extended Kalman filtering dynamic estimation is performed, wherein adaptive robust extended Kalman filtering is adopted toperform dynamic estimation on a power angle and an electric angle of the generator in an electromechanical transient process according to a generator state space model. Through the method, an estimation error upper limit brought by system parameter uncertainty can be effectively defined, and by the adoption of the adaptive technology to perform adaptive estimation on the parameters, the problem that the error upper limit is difficult to select through traditional robust extended Kalman filtering is avoided. Therefore, compared with existing methods, the method has higher robustness and higherestimation precision.

Description

technical field [0001] The invention relates to a power system analysis method, in particular to a generator dynamic estimation method. Background technique [0002] In recent years, the synchronized phasor measurement unit (PMU) based on the wide area measurement system (WAMS) has been gradually promoted and applied. analysis is possible. However, as a measurement system, WAMS will inevitably be affected by random interference and other factors during the measurement process, resulting in the pollution of measurement data. Therefore, the measured raw 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 predictive function, can formulate corresponding control strategies for possible future changes of the system. Therefore, improving the tracking accuracy of dynamic state estimation of g...

AI Technical Summary

Problems solved by technology

However, in the actual power system analysis, it is difficult to obtain the statistical laws satisfied by the noise accurately, and some parameters and input values ​​of the model are inaccurate or unknown, these uncertain factors will seriously affect the performance of the state estimator , so that the results of state estimation cannot be accurately obtained

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