基于信息扩散估计的负荷综合建模

为了满足电力系统先验仿真的要求,负荷模型需要概括不同时刻的负荷特性。针对负荷建模实测数据较少的情况,提出一种基于信息扩散估计的负荷综合建模方法。该方法首先利用负荷节点采集到的小样本单条扰动数据建立负荷模型,模型中仅辨识灵敏度大的参数以降低参数分散性的影响。然后基于信息扩散理论估计负荷模型参数的概率密度函数,利用K-S方法检验所得概率密度的正确性。最后根据此概率密度函数估计综合负荷模型的参数。该方法可以对现场实测的有限数据样本信息进行拓展,通过对样本信息的深度挖掘来估计模型参数的总体特征,使得所建负荷模型具有良好的泛化能力。

Comprehensive load modeling based on information spread estimation

In order to meet the requirements of prior power system simulation, it is necessary to summarize the load characteristics at different time. Since the measured data are usually scarce, this paper proposes a new strategy to build the comprehensive load model. Firstly, single measured data are used to build the load model at every sampling time. In order to reduce the dispersibility of the parameters, only the parameters of high sensitivity are identified. And then the probability density functions of load model parameters are estimated based on the information spread principle. The correctness of the probability density functions is verified using K-S testing method. Finally, the parameters of the comprehensive load model are evaluated according to the probability density functions. This method can expand the limited measured data information. The general characteristics of the model parameters are estimated by deep analysis of the sampling information. A case study is presented to verify that the comprehensive load model established by the proposed mathods has good generalization ability.