高压串联谐振变换器广泛应用于电容器充电、静电除尘等系统中。然而,高压变压器寄生电容的存在,使得客观上并不存在理想的高压串联谐振变换器。定量分析了高压高频变压器的寄生电容对工作于断续谐振电流模式(discontinuous current mode...高压串联谐振变换器广泛应用于电容器充电、静电除尘等系统中。然而,高压变压器寄生电容的存在,使得客观上并不存在理想的高压串联谐振变换器。定量分析了高压高频变压器的寄生电容对工作于断续谐振电流模式(discontinuous current mode,DCM)的串联谐振变换器特性的影响,这些特性包括临界断续谐振频率、归一化输出电流和软开关。当考虑高压变压器寄生电容后,串联谐振变换器实际上已经演变为LCC串并联谐振变换器。通过对DCMLCC谐振变换器在不同工作阶段的数学分析、推导和归一化处理,得到了具有封闭形式的电路特性的表达式。通过分析发现,随着等效电压增益的增加,DCM LCC谐振变换器的正向和反向谐振过程均由两元件谐振向三元件谐振过程转变,临界断续频率升高。以图形曲线的方式给出了量化的分析结果。通过比较两类典型的控制方法可知,第二类典型控制方法具有更高的电流输出能力和能量传输效率,是一种优化的控制方法。所得分析结果可为工作于断续谐振电流模式的高压串联谐振变换器的设计提供参考,特别对电容充电和静电除尘电源具有工程应用价值。展开更多
A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as...A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.展开更多
文摘高压串联谐振变换器广泛应用于电容器充电、静电除尘等系统中。然而,高压变压器寄生电容的存在,使得客观上并不存在理想的高压串联谐振变换器。定量分析了高压高频变压器的寄生电容对工作于断续谐振电流模式(discontinuous current mode,DCM)的串联谐振变换器特性的影响,这些特性包括临界断续谐振频率、归一化输出电流和软开关。当考虑高压变压器寄生电容后,串联谐振变换器实际上已经演变为LCC串并联谐振变换器。通过对DCMLCC谐振变换器在不同工作阶段的数学分析、推导和归一化处理,得到了具有封闭形式的电路特性的表达式。通过分析发现,随着等效电压增益的增加,DCM LCC谐振变换器的正向和反向谐振过程均由两元件谐振向三元件谐振过程转变,临界断续频率升高。以图形曲线的方式给出了量化的分析结果。通过比较两类典型的控制方法可知,第二类典型控制方法具有更高的电流输出能力和能量传输效率,是一种优化的控制方法。所得分析结果可为工作于断续谐振电流模式的高压串联谐振变换器的设计提供参考,特别对电容充电和静电除尘电源具有工程应用价值。
基金supported by National Natural Science Foundation of China(Nos.51107115,11347125,51407156)China Postdoctoral Science Foundation(Nos.20110491766,2014M551735)
文摘A reliable, efficient and economical power supply for dielectric barrier discharge （DBD） is essential for its industrial applications. However, the equivalent load parameters complicare the design of power supply as they are variable and varied nonlinearly in response to varied voltage and power. In this paper the equivalent electrical parameters of DBD are predicted using a neural network, which is beneficial for the design of power supply and helps to investigate how the electrical parameters influence the equivalent load parameters. The electrical parameters includ- ing voltage and power are determined to be the inputs of the neural network model, as these two parameters greatly influence the discharge type and the equivalent DBD load parameters which are the outputs of the model. The voltage and power are decoupled with pulse density modula- tion （PDM） and hence the impact of the two electrical parameters is discussed individually. The neural network model is trained with the back-propagation （BP） algorithm. The obtained neural network model is evaluated by the relative error, and the prediction has a good agreement with the practical values obtained in experiments.
