【摘要】 针对航天产品供电系统中Superbuck变换器电路模型阶次难以识别、小信号模型获取频率特性困难问题，提出基于主动变载技术的Superbuck变换器电路模型阶次识别方法。通过主动变载建立多种Superbuck变换器输出电压数据样本库；采用变分模态分解和符号动力学熵技术提取数据特征，结合高斯云模型的数字特征，评估并优化特征提取效果，建立高斯云模型；计算待测Superbuck变换器数据对各阶次高斯云模型的隶属度，基于最大隶属度原则确定电路模型阶次。以3类不同阶次的Superbuck变换器为例，开展阶次识别研究，验证了所提方法的有效性。更多还原

【Abstract】 Aiming at the problems of difficulty in recognizing the order of Superbuck converter circuit model and difficulty obtaining the frequency characteristics of the small-signal model in the power supply system of aerospace products,a method of recognizing the order of the Superbuck converter circuit model based on active load variationtechnology is proposed. A variety of Superbuck converter output voltage data sample library is established by active load variation. The data features are extracted by using variational modal decomposition(VMD) and symbolic dynamic entropy(SDE),and the Gaussian cloud model(GCM) is established by combining the digital features and evaluating and optimizing the effect of the feature extraction. The affiliation of the Superbuck converter data to be measured to the GCM is calculated,and the circuit model order is determined based on the principle of maximum affiliation. The order identification study is carried out by taking three types of Superbuck converters with different orders as examples,and the effectiveness of the proposed method is verified.更多还原