.png){kind=icon}
返回
Data-Driven Compensation Algorithm for Optimizing Power Quality in Interleaved Boost PFC
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3444056.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
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
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
PWM Control of N-Phase Interleaved Active Front-End Boost Stage-Based Impedance Source InverterN相交错式有源前端升压级阻抗源逆变器的PWM控制
PREAI
分享收藏
An Artificial Intelligence Approach for Real-Time Tuning of Weighting Factors in FCS-MPC for Power Converters用于实时调整功率转换器的fcs-mpc中的加权因子的人工智能方法
PREAI
分享收藏
查看更多内容