  The recent advances in power plants and energy resources have extended the
applications of DC-DC converters in the power systems (especially in the
context of DC micro-grids). Parameter identification can extract the parameters
of the converters and generate accurate discrete simulation models. In this
paper, we propose a measurement-based converter parameter calibration method by
an adaptive Approximate Bayesian Computation with sequential Monte Carlo
sampler (ABC SMC), which estimates the parameters related to passive and
parasitic components. At first, we propose to find suitable prior distribution
for the parameter which we don't know the prior information about them. With
having prior distributions, we can use the ABC SMC to find the exact values of
the parameters of the converter. We chose the distance function carefully and
based on the simulations we assigned the best method for the threshold
sequencing. For improving the computationally of the algorithm, we propose an
adaptive weight that helps the algorithm to find the optimal values with fewer
simulations. The effectiveness of the proposed method is validated for a DC-DC
buck converter. The results show that the proposed approach can accurately and
efficiently estimate the posterior distributions of the buck parameters subject
to gross errors in the prior distributions of the parameters. The proposed
algorithm can also be applied to other parameter identifications and
optimization applications such as rectifiers, filters, or power supplies, among
others.
