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引用次数: 1
摘要
本文讨论了用线性判别分析对局部放电类型进行分类的问题。实验室产生的四种放电类型分别是空气高压侧电晕、空气低压侧电晕、空气表面电晕和内部放电。该线性判别分析模型中使用的自变量主要来自指纹分析得到的Phi - q - n PD模式的偏度、峰度、不对称性和相互关系,这是一种用于PD测量的数字信号处理技术。本文还应用了分步模型选择技术,将10个自变量减少到8个自变量,不仅降低了模型估计的复杂性,而且使预测模型的准确率保持在98.7%。
Linear Discriminant Analysis for Partial Discharge Classification on High Voltage Equipment
This document is addressing a problem in classifying partial discharge types using linear discriminant analysis. Four PD types generated in the lab are corona at high voltage side in air, corona at low voltage side in air, surface in air, and internal discharge. The independent variables used in this linear discriminant analysis model mainly are from skewness, kurtosis, asymmetry, and cross correlation following the Phi - q - n PD patterns obtained from the fingerprint analysis which is a digital signal processing technique for PD measurement. This document also applied step wise model selection technique to reduce from 10 independent variables to 8 independent variables that not only reduces the complexity of the model estimated but also retains the accuracy of this predictive model to 98.7 percent.