负荷模型参数简化对负荷分类结果的影响

Impact of Model Parameter Reduction on Load Classification

负荷模型作为电力系统仿真的主要元件之一,越来越受到重视,但目前动态负荷模型的参数个数较多,严重制约了负荷模型参数辨识的精度。为了解决这个问题,通常的做法是对负荷模型参数进行简化;另外,为了消除负荷时变性对建模的影响,需要对负荷进行分类,但参数简化对负荷分类结果是否有较大的影响却不得而知。为了探讨这个问题,应用基于空间响应的聚类分析法对负荷进行了分类,然后应用轨迹灵敏度方法对负荷模型参数进行了简化,重新对数据进行了分类并比较了参数简化前后分类的结果,以一实际系统分析为例,结果表明参数简化前后分类误差没有较大幅度增加,验证了参数简化的有效性。

As one of the important components in power simulation,load model has been getting more and more attention.But current load models have lots of parameters,which significantly affects the model＇s identification accuracy.To solve this problem,the common practice is to reduce the parameters and load classification is always utilized to eliminate the time-dependent characteristics of the power load.However,it is not known yet how the parameter reduction effects the load classification.Cluster analysis is firstly carried out in this paper to classify the load data,and then the trajectory sensitivity analysis is utilized to reduce the model parameters.Reclassification of the load data is then made after parameter reduction and a comparison is conducted between the classification results before and after parameter reduction.A case study indicates that the parameter reduction doesn＇t increase the classification error significantly and proves the efficiency of the proposed parameter reduction method