On-line Dynamic Electric Load Modeling Based on Unscented Kalman Particle Filter
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Abstract
The time-variation of electric load has a great influence on the real-time dynamic simulation of power systems. In order to improve the accuracy of real-time dynamic simulation and analysis, an on-line dynamic electric load modeling method based on unscented kalman particle filter is proposed in this paper. That is, the unscented kalman particle filter is used to estimate the parameters of an exponential dynamic load model structure online. In this way, the parameters of the dynamic lad model can be adjusted online according to the measured data, and the real-time load characteristics are tracked effectively. The measurements from the digital simulation platform and the actual power systems are used to test the effectiveness of the proposed method, respectively. The results provided by the proposed online parameter identification method is accurate, the real-time change load characteristics of the actual power system also can be described precisely.
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