Estimation of composite load model parameters as a constrained nonlinear problem

This paper presents the results of application of sequential quadratic programming to the estimation of the unknown composite load model parameters. Traditionally applied estimation methods, such as nonlinear least squares or genetic algorithms, suffer from a number of issues. Genetic algorithms exhibit premature convergence and require high computational resources and nonlinear least squares method is very sensitive to the initial guess and can diverge easily. This paper provides a comparison of all three methods based on computer-generated signals serving as field measurements. Accuracy and precision are assessed as well as computational requirements.