基于多种智能方法的变压器故障综合诊断模型

鉴于粗糙集处理不完备信息的有效性、范例推理查询匹配的直观性以及比值法的简洁性,提出了基于多种智能方法的变压器故障综合诊断方法。综合考虑了油中溶解气体分析与电气试验等多种故障征兆,在分析大量变压器故障案例的基础上,建立了基于粗糙集、范例推理及比值法的综合诊断模型。该模型采用3层结构对变压器故障逐步进行细分,即使在不完备信息时也有助于为现场提供较有效的维修建议。该模型具有计算速度快、正判率高、结果直观等优点。实例也表明了该方法的有效性。

A Multi-AI Methods Based Model for Synthetic Diagnosis of Transformer Faults

Taking into account the efficiency of the rough set in incomplete information processing, the intuitiveness of querying and matching by case-based reasoning and the convenience of the ratio method, an approach based on multi-artificial intelligence methods for diagnosing transformer faults is proposed. By comprehensively considering the symptoms from DGA and electrical tests and analyzing large numbers of fault cases, a model for synthetic diagnosis based on the rough set, case-based reasoning and ratio method is developed. A three-layer structure has been constructed for this model to analyze the detailed fault of a transformer step by step. It is helpful to providing field personnel with effective advice even if the fault information may be incomplete. This model has such advantages as high speed of calculation, high correct rate, intuitional results, etc. Examples also show the effectiveness of the method.