  Accurate identification of parameters of load models is essential in power
system computations, including simulation, prediction, and stability and
reliability analysis. Conventional point estimation based composite load
modeling approaches suffer from disturbances and noises and provide limited
information of the system dynamics. In this work, a statistic (Bayesian
Estimation) based distribution estimation approach is proposed for both static
(ZIP) and dynamic (Induction Motor) load modeling. When dealing with multiple
parameters, Gibbs sampling method is employed. In each iteration, the proposal
samples each parameter while keeps others fixed. The proposed method provides a
distribution estimation of load models coefficients and is robust to
measurement errors.

Systems and Control