电力系统保护与控制Issue(13)：110-115,6.
基于相关系数矩阵和概率神经网络的局部放电模式识别
Pattern recognition for partial discharge based on correlation coefficient matrix and probabilistic neural networks
摘要
Abstract
A new dimension reduction method based on correlation coefficient matrix is proposed aimed at the high-dimension of characteristic parameters in the process of pattern recognition for partial discharge in power transformer. The correlation coefficient matrix (CCM) is constructed using parameters extracted from partial discharge signal in power transformer. The parameters which have similar classification ability to each other are deleted with the help of correlation analysis among 18 characteristic parameters in CCM. Six parameters which have higher classification capabilities are extracted using the critical index and are used as the inputs for pattern classifiers of probabilistic neural networks. The results show that the parameter dimension is reduced and the classifier construction is simplified, and the recognition effect is better than that of the traditional back propagation neural network in the condition of small samples.关键词
相关系数矩阵/概率神经网络/变压器/局部放电/模式识别Key words
correlation coefficient matrix/probabilistic neural networks/power transformer/partial discharge/pattern recognition分类
信息技术与安全科学引用本文复制引用
苑津莎,尚海昆,王瑜,靳松..基于相关系数矩阵和概率神经网络的局部放电模式识别[J].电力系统保护与控制,2013,(13):110-115,6.基金项目
国家自然科学基金（61204027）；中央高校基本科研业务费专项资金资助(13XS26) （13XS26）
