Zhang Weizheng

The fault diagnosis of power transformer based on rough set theory

A transformer is one of the most important units in power networks and its fault diagnosis is quite significant. Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty. It has received much attention of the researchers around the world. Rough set theory has been successfully applied to many areas including pattern recognition, machine learning, decision support, process control and predictive modeling. Due to incompleteness and complexity of fault diagnosis for power transformer, a specific fault diagnostic model based on rough set theory is presented in this paper. After the statistical analysis of the collected fault examples of oil-immersed power transformer and the use of rough set theory to reduce result, diagnosis rules are acquired and they could be used to improve the condition assessment of power transformer. The fault diagnose inference model was built based on the advantage of effectively simple decision rules and easy reality of rough sets. It simplifies the diagnose rules with no affecting the effect of diagnose. The significant advantage of the new method is that it can discriminate the indispensable alarm signals from dispensable ones that would not affect the correctness of the diagnosis results even if they are missing or erroneous.

The application of compound networks in fault diagnosis of power transformer

Using the concepts of typical gass concentration and cumulative frequency in analysis of the reliability data for dealing with the pretreatment of data of DGA, two new normalized methods which named characteristic normalization and mix normalization are presented in this paper. The Fisher rule to evaluate the results of the two pretreatment methods is also introduced. The evaluation of the results indicates that both of the two data pretreatment methods can achieve the purpose of big difference in the value of mean between classes and small difference in dispersion of a class. The DGA data of the failure transformers are treated by different normalization methods as the training samples, and then the samples are trained in the compound neural networks which use the CP algorithm. The diagnosis results of the test samples indicate that the new methods may help to improve the precision of network diagnosis.