Multi-objective Evolutionary Approach to Grey-Box Identification of Buck Converter
This work addresses grey-box identification for power electronics systems, offering an incremental improvement by explicitly incorporating a priori knowledge into model selection.
The study tackled the problem of modeling a DC-DC buck converter with limited dynamic data by integrating known static behavior into a multi-objective framework, resulting in parsimonious models that accurately capture both dynamic and static responses over a wide input range.
The present study proposes a simple grey-box identification approach to model a real DC-DC buck converter operating in continuous conduction mode. The problem associated with the information void in the observed dynamical data, which is often obtained over a relatively narrow input range, is alleviated by exploiting the known static behavior of buck converter as a priori knowledge. A simple method is developed based on the concept of term clusters to determine the static response of the candidate models. The error in the static behavior is then directly embedded into the multi-objective framework for structure selection. In essence, the proposed approach casts grey-box identification problem into a multi-objective framework to balance bias-variance dilemma of model building while explicitly integrating a priori knowledge into the structure selection process. The results of the investigation, considering the case of practical buck converter, demonstrate that it is possible to identify parsimonious models which can capture both the dynamic and static behavior of the system over a wide input range.