摘要翻译：
准确预测输出电压纹波幅度的模型对于性能指标苛刻的应用是必不可少的。采用离散域精确离散化建模方法，分析了串联谐振变换器(SRC)直流输入纹波对输出纹波的影响。提出了一种新的离散状态空间模型和考虑3个状态变量的SRC小信号模型。从小信号模型出发，导出了输入与输出纹波之间的声敏度传递函数。通过对AS传递函数的分析，得到了一个谐振峰值，并推导出了不同SRC分量值下输入纹波的AS谐振频率的表达式。进一步分析表明，SRC参数存在一组值，这形成了一个设计区域，在该区域内，SRC对输入纹波提供的归一化增益在任意输入纹波频率下都小于1。提出了一种在SRC输入端引入变频纹波的试验装置，用于AS传递函数的实验评估。研究了杂散参数对AS增益、AS谐振频率和SRC槽谐振频率的影响。设计了一台功率为10kW的SRC。用导出的模型进行的分析、模拟和实验结果发现是非常吻合的。
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英文标题：
《Small Signal Audiosusceptibility Model for Series Resonant Converter》
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作者：
Subhash Joshi T.G. and Vinod John
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Models that accurately predict the output voltage ripple magnitude are essential for applications with stringent performance target for it. Impact of dc input ripple on the output ripple for a Series Resonant Converter (SRC) using discrete domain exact discretization modelling method is analysed in this paper. A novel discrete state space model along with a small signal model for SRC considering 3 state variables is presented. The audiosusceptibility (AS) transfer function which relates the input to output ripple is derived from the small signal model. Analysis of the AS transfer function indicates a resonance peak and an expression is derived connecting the AS resonance frequency for input ripple with different SRC component values. Further analysis is done to show that a set of values for SRC parameter exists, which forms a design region, for which the normalized gain offered by the SRC for input ripple is less than unity at any input ripple frequency. A test setup to introduce the variable frequency ripple at the input of SRC for the experimental evaluation of AS transfer function is also proposed. Influence of stray parameters on AS gain, AS resonance frequency and on SRC tank resonance frequency is addressed. An SRC is designed at a power level of 10kW. The analysis using the derived model, simulations, and experimental results are found to be closely matching.
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PDF链接：
https://arxiv.org/pdf/1802.0693