摘要
以变电站的开关继电保护信息为基础,提出了一种基于粗糙集理论和贝叶斯分类器的变电站故障诊断方法。首先利用粗糙集理论的知识约简和处理不确定信息的能力,对变电站的故障诊断知识进行挖掘,实行属性优选,再运用朴素贝叶斯分类器对故障诊断知识进行模式识别。将其应用于变电站故障诊断专家系统中,应用结果显示了该方法能有效地缩小问题求解规模和较强的抗干扰能力,是一种有效的变电站故障诊断方法。
On the basis of switch and relay protecting information of substation, an approach to substation fault diagnosis is proposed based on rough sets theory and bayesian classifier. Namely, rough set is applied to mine fault diagnosis ,knowledge of substation and implement predominant attributes selection based on its abilities of knowledge reduction and disposing indeterminate information, then fault is identified though bayesian classifier. Eventually, being used in fault diagnosis expert systems of substation, the application results show the approach minimizes the problem solving scale and owns excellent anti-inference capabilities, and is an effective method for fault diagnosis of substation.
出处
《计算机工程与设计》
CSCD
北大核心
2006年第16期3099-3101,共3页
Computer Engineering and Design
关键词
变电站
粗糙集
贝叶斯分类器
故障诊断
预测
substation
rough set
bayesian classifier
fault diagnosis
prediction
