SF_6负极性直流局部放电分解特性与组分特征提取

Decomposition Characteristics of SF_6 Under Negative DC Partial Discharge and Component Features Extraction

为了研究SF_6在不同类型负极性直流局部放电(partial discharge,PD)下的分解特性,进而建立SF_6分解特性与缺陷类型之间的关联特性。在自建的SF_6负极性直流PD分解实验平台上,通过系列实验研究得到了SF_6在4种不同典型绝缘缺陷引起的负极性直流PD下的分解数据。研究表明:4种典型绝缘缺陷PD下SF_6均会发生分解,生成CF_4、CO_2、SO_2F_2、SOF_2和SO_2这5种稳定组分,且4种缺陷下组分体积分数以及由分解组分构造而成的特征比值(简称特征组分比值)随放电时间均具有各自不同的变化规律,可以用于缺陷类型表征。为了验证这两类特征参量对缺陷类型表征的有效性,使用谱聚类算法分别对两类特征量进行聚类处理,结果表明,两类特征参量均能对缺陷类型进行有效反映,且特征组分比值参量更加适合用于缺陷类型识别。

To study the decomposition characteristics of SF6 under different types of negative DC partial discharge （PD）, then to establish the correlation between the SF6 decomposition characteristics and the PD type, SF6 decomposition experiments were conducted on the SF6 decomposition platform under four different types of PD. The results show that the SF6 gas will decompose under the negative DC PD caused by the four defects and generate five stable decomposed components, namely, CF4, C02, S02F2, SOF2, and S02. And the concentrations of the SF6 decomposed components and the characteristic component ratio have different change rules versus discharging time, which could be used for PD type characterization. In order to choose characteristic parameters that can effectively characterize the PD types, the SF6 decomposed component concentration and the characteristic component ratio were treated by the spectral clustering algorithm to recognize the PD types. It is concluded that these two characteristic parameters both can effectively reflect PD type, and the characteristic component ratio behaves better.