GIS局部放电深度分层放电类型诊断方法研究

现有的GIS局部放电类型诊断主流采用单一分类器直接进行多类型划分,该方法对类间交叉重叠区域敏感,且受单一分类器固有缺陷的影响。文中提出了一种深度分层放电类型诊断方法,以逐层二分决策实现多类划分,在分层决策中优先进行良性样本的区分,将交叉重叠区域分类问题放至深层节点进行,且在每个二分节点处可择优选用不同分类器。设计了5种典型的GIS放电模型,从放电PRPD谱图、U-Δt序列谱图的统计特征、图像特征出发,构造了16个特征参量,探索了不同分层深度值下的诊断分类正确率,并与传统直接分类方法进行了比较。结果表明:深度分层诊断相比于直接识别诊断,总体识别正确率提高了20%,尤其对直接识别诊断误判率大的沿面、颗粒类缺陷,识别正确率提升明显(30%)。

Recognition of Partial Discharge in GIS Based on Depth Stratification

The methods available for partial discharge diagnosis use single classifier for multi-type division. These methods are sensitive to cross - overlapped regions and greatly affected by inherent defects of the single classifier. This paper proposes a depth stratification diagnosis method of partial discharge, achieves multi-class division by layer-by-layer dichotomy decision. Benign samples are recognized preferentially in hierarchical decision making, and classify crossover regions into deep nodes. Different classifiers may be chosen at each dichotomous node. Five typical GIS discharge models are designed, PRPD spectra and U-St sequence spectra are formed, and 16 characteristic parameters are constructed. The diagnostic classification accuracy under different depth is explored, and compared with traditional direct classification methods. The results show that the accuracy of the depth stratification recognition is improved by 20% compared with the direct recognition diagnosis, especially for the surface and particle defects( improved by 30%).