电力变压器固体绝缘故障的诊断

通过对故障涉及固体绝缘时其它特征气体组分与CO、CO2间的伴生增长情况研究，提出了一种动态分析变压器绝缘故障的方法，着手建立故障气体的增长模式，为预测故障的发展提供新的判据。

一台冶炼变压器内部放电故障分析

文中阐述用现场试验结果,结合变压器油色谱分析方法手段,分析判断一起电炉变压嚣内部高能放电故障。

电力变压器固体绝缘故障的诊断

为了使设备的外形尺寸保持在可接受的水平，现代变压器的设计采用了更为紧凑的绝缘方式。这就要求显著升高其运行中内部各组件间的绝缘所承受的热和电应力水平。该文通过对故障涉及固体绝缘时其它特征气体组分与CO、CO2间的伴生增长情况研究，提出了一种动态分析变压器绝缘故障的方法，着手建立故障气体的增长模式，为预测故障的发展提供新的判据。

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.