基于数据挖掘模型的高压输电线系统故障诊断

在大多数故障诊断系统中，由于诊断所依据的实时信息在其形成和传递过程中都有可能产生信息的畸变，从而导致故障诊断结果的错误。文中提出利用基于粗糙集理论的数据挖掘模型来处理实时输入信息的畸变和实现输电线系统的故障诊断，它是依据粗糙集定性分析能力对知识域的数据集进行分析，粗糙集的约简是通过遗传算法求取。还给出了构造测试样本的理论准则，从而使检验故障诊断系统的容错性能具有保证和真正的实用价值。通过仿真测试证明，基于数据挖掘模型的故障诊断与基于神经网络模型的故障诊断相比，具有更高的容错性能。

A NEW DATA MINING APPROACH FOR FAULT DIAGNOSIS OF HIGH VOLTAGE TRANSMISSION LINE BASED ON ROUGH SET THEORY

In most practical application of fault diagnosis system ,misjudgem ent may be caused by real- time information distorted in the process of generation and transfer.This paper presents rough set RS based data mining m ethod to deal with distorted inform ation and to im plem ent the fault diagnosis of HV transm ission line system HVTL S .In this approach,the qualitative analysis ability of RS is used to analyze knowledge region data set and the reductions of RS are solved by a genetic algorithm GA .At same time,this paper proposes the criterion to build testing sam ples,in order to get the assurance of fault tolerance perform ance of tested diagnosis system and have practical application potential of studied system .The higher fault tolerance perform ance of the proposed approach is confirmed through the com parison with that of NN- model based fault diagnosis system.