The condition estimate of power transformer based on rough set theory
Zhang Weizheng, Lanjun Yang, Yanming Li, Fazhan Liu, Chunyan Ma · IET Conference Publications · 2009
A transformer is one of the most important units in power networks; thus, condition estimate of transformers is quite significant. 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 condition estimate for power transformer, a specific model based on rough set theory is presented in this paper. After the statistic analysis on the collected fault examples of oil-immersed power transformer and using rough set theory to reduce result, estimate rules are acquired and they could be used to improve the condition assessment of power transformer. The condition estimate inference model was built based on the advantage of effectively simple decision rules and easy reality of rough sets. 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 estimate results even if they are missing or erroneous. (4 pages)