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Measurement-Based Parameter Identification of DC-DC Converters with Adaptive Approximate Bayesian Computation

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.

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