基于DGA的反馈云熵模型电力变压器故障诊断方法研究

为了解决变压器故障诊断中存在的随机性和模糊性问题，提出了基于反馈云熵模型的电力变压器故障诊断新方法。通过对大量电力变压器故障征兆及故障类型的统计分析，并将其视作云滴输入贝叶斯反馈逆向云发生器中，得到故障特征气体的云模型参数值，构建变压器故障诊断标准正态云模型。将云关联系数和信息熵理论有机结合起来，降低了对单个标准正态云模型的依赖性，充分挖掘变压器油中溶解气体所包含的故障信息，提高了变压器故障诊断的准确率。通过不断丰富输入样本、修正云模型参数值的方法，可以进一步提高模型诊断效果。实例分析结果表明该模型的故障诊断准确率较高，并具有较好的理论价值和应用前景。

Dissolved gas analysis based feedback cloud entropy model for power transformer fault diagnosis

In order to solve the problem of randomness and fuzziness in power transformer fault diagnosis, a new fault diagnosis method for power transformer based on feedback cloud entropy model is proposed. After the statistic analysis of the collected fault examples of chromatographic data for transformer oil, which is put into Bayesian feedback backward cloud generator as cloud drop, the transformer fault diagnosis standard normal cloud model is built based on parameter values of fault characteristic gases cloud model. The model integrates cloud correlation coefficient and information entropy theory, reduces the dependence on the single standard normal cloud model and digs more information of the dissolved gases in transformer oil, and improves the accuracy of transformer fault diagnosis. By increasing training samples and correction cloud model parameter, the effectiveness of the model can be further enhanced. The results of example show that the model has higher accuracy, and has well theoretical value and application prospects.