Data-Driven Compensation Algorithm for Optimizing Power Quality in Interleaved Boost PFC

Abstract

Nonlinearities of inductor’s soft magnet, converter’s time-varying mode and control delays limit the grid-side power quality improvement capability of interleaved boost Power Factor Correction (PFC) circuit. The traditional internal model principle-based approaches are widely used to improve the power quality, but the expense is the reduce of certain stability. Hence, this paper proposes a data-driven online compensation method to address this trade-off between control accuracy, power quality and stable margin. This method involves recording control data of a multi-frequency proportional resonant (PR) controller under various input conditions. The collected data is preprocessed and used to establish a regression compensation model through multivariate nonlinear regression. Finally, this regression model is applied to the compensation loop of a lower-order controller to improve power quality of the PFC while ensuring sufficient stable margin. Experiments verify the practical feasibility and the effectiveness of the proposed data-driven control method.

Keywords

• Power factor correction
• soft saturation characteristics
• data-driven
• multivariate nonlinear regression