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Data-Driven Compensation Algorithm for Optimizing Power Quality in Interleaved Boost PFC
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DOI：10.1109/ACCESS.2024.3444056.png)
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Abstract
En 中文
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 quality
Data models
Accuracy
Table lookup
Integrated circuit modeling
Costs
Inductors
Power factor correction
Multivariate regression
soft saturation characteristics
data-driven
multivariate nonlinear regression
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
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