Parameter Identification of Load Model Using Scatter Plot Based Global Sensitivity Analysis

Sensitivity analysis is an important step in power system parameter identification. It is used to find the key parameters that need to be identified and have a greater impact on the model output among the numerous model parameters. However, the existing parameter identification methods simultaneously identify all key parameters and do not make full use of the difference between high and low parameter sensitivity to further improve the identification accuracy. In this paper, the scatter plot method of global sensitivity analysis is improved, and a plane observation method of high dimensional parameter optimization space is proposed, and it is applied to the parameter identification of the synthesis load model. Based on this method, the search range of parameters can be continuously reduced according to the inflection point of the envelope of the lowest point of the planar graph in each parameter optimization subspace. The value of some high-sensitivity parameters can be determined directly through the narrowing search range. The method proposed in this paper can be used to further reduce the parameters that need to be identified at the same time and may help to further improve the accuracy of parameter identification.