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首页> 外文期刊>MATEC Web of Conferences >Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter
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Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

机译：基于可变遗忘因子递推最小二乘算法的Boost变换器在线电感电容辨识

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摘要

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations.
机译：升压转换器的控制性能受重要组件参数（例如电感和电容）变化的影响。提出了一种基于可变遗忘因子递推最小二乘（VFF-RLS）算法的升压变换器在线电感和电容辨识方法。首先，介绍了精确的电感和电容识别模型以及RLS算法。为了平衡稳态识别精度和参数跟踪能力，研究了遗忘因子控制技术。通过恢复算法误差信号中的系统噪声，可以动态计算遗忘因子的值。此外，由于采样率远低于现有的识别方法，因此该算法对于低成本应用是实用的。最后，通过实验验证了所提算法的有效性。实验结果表明，该算法具有良好的跟踪电感和电容变化的性能。

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