基于模糊关联规则挖掘的电力变压器故障诊断方法

Fault Diagnosis of Power Transformer Based on Fuzzy Association Rules Mining

在对变压器故障的状态监测过程中,故障特征量在某些情况具有不确定特性,所以判断变压器运行状态的过程具有一定的模糊性,确定性的关联规则难以有效地表达特征量与故障之间的关系。为建立更准确有效的变压器故障诊断模型,文中对经典的Apriori算法进行优化,在利用主成分分析对多源参数进行优选后,将其扩展到包含模糊属性的事务当中,并与传统IEC三比值相结合作为特征量共同提取规则,所建模型的执行效率及准确度相比原Apriori算法更高,且可进一步运用至变压器多源参数的关联规则挖掘。结果表明,结合三比值共同提取的规则,其正判率将大幅提高,且模糊理论与三比值相结合所提取的规则,其正判率高于经典集合理论与三比值相结合所提取的规则,最后经由Apriori算法提取的复合规则,其正判率较高。利用文中所建立的模糊关联规则模型可更准确高效地诊断变压器故障,从而有效服务于电力设备的运维工作。

In the process of condition monitoring of transformer,characteristic quantities of a fault are uncertain in some cases.Therefore,the diagnosing process of transformer operation status is somewhat fuzzy,so the definite association rules fail to reflect effectively the relation between the characteristic quantities and the fault.In order to establish a more accurate and effective model for transformer fault diagnosis,we extend the optimized classic Apriori algorithm to the affairs containing fuzzy attributes after using the principal component analysis to optimize the multisource parameters,and combine it with the IEC three-ratio code as the characteristic quantity to extract rules.The efficiency and accuracy of the established model are higher compared with that of the original Apriori algorithm.The model can also be applied to association rules mining of multiple parameters of the transformer.In conclusion,the common extraction rule of characteristic quantities on the basis of combining Apriori algorithm with IEC 3-ratio code has higher hit ratio,and the characteristic quantity extraction rule combining the fuzzy theory with the IEC 3-ratio code offers higher hit ratio than that rule combining the classic set theory with the IEC 3-ratio code.In summary,the combined extraction rule of characteristic quantities via the Apriori algorithm achieves higher hit ratio than that via IEC 3-ratio code.The present fuzzy association rule model can diagnose transformer faults more accurately and efficiently.