Improvement of composite load modeling based on parameter sensitivity and dependency analyses

This paper presents an effective optimization scheme for the measurement-based load modeling based on the sensitivity analysis of composite load model parameters. Each parameter of load model has different effects on its dynamic response. Moreover, some parameters are insensitive to the change of others. To estimate the dynamic interactions between parameters, their sensitivity is analyzed by using the eigenvalues of Hessian matrix used in the optimization algorithm. Also, the linear dependence between two load model parameters is then identified by examining the condition number of Jacobian matrix. With this parameter analysis, the performance of optimization process for measurement-based composite load modeling is improved by reducing the number of necessary parameters to consider. The performance of proposed method is verified with the practical data measured at a feeder in a real substation.