基于不敏卡尔曼粒子滤波的动态电力负荷在线建模
On-line Dynamic Electric Load Modeling Based on Unscented Kalman Particle Filter
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摘要:
电力负荷的时变性对电力系统实时动态仿真分析具有较大影响。为了提高实时动态仿真分析的精度，基于不敏卡尔曼粒子滤波提出一种动态电力负荷在线建模方法。针对一种指数型动态负荷模型结构，利用不敏卡尔曼粒子滤波算法对其参数进行在线辨识。通过这种方式，可以根据实时采集的量测数据在线修正动态负荷模型的参数，从而追踪电力负荷的实时变化特性。分别利用动态仿真平台和实际电力系统的量测数据进行仿真分析，结果表明了所提方法具有较高的在线参数辨识精度，并能对实际电力负荷的实时变化特性进行准确的描述。 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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