基于粗糙集-贝叶斯的电网故障诊断方法  

Power System Faults Diagnosis Based on Rough Sets-Bayesian Networks

作　　者：宋耐超 陈素霞[2] 

机构地区：[1]许昌供电公司,河南许昌461000 [2]三明学院数学与计算机系,福建三明365004

出　　处：《化工自动化及仪表》2011年第7期816-819,共4页Control and Instruments in Chemical Industry

摘　　要：提出了一种基于粗糙集-贝叶斯的电网故障诊断方法,采用粗糙集信息表约简方法对电网故障特征进行约简,获取了电网故障诊断最小决策表,然后针对最小诊断决策规则建立贝叶斯网络模型,利用贝叶斯网络节点之间的权重关系提高电网故障诊断的效率和正确率。A Rough Sets theory and Bayesian networks-based faults diagnosis method for the power system was proposed to obtain the minimal fault diagnosis decision-making tables of power system,and to have the fault characteristics of the power system reduced by making use of the rough set reduction algorithm,and have its Bayesian network constructed and the accuracy and efficiency of power system fault diagnosis improved by adjusting the weighting factor of the nodes in Bayesian network.Simulation results show that the proposed method is simple and practical.

关 键 词：粗糙集理论 贝叶斯网络 电网故障诊断 权重关系 

分 类 号：TP306.3[自动化与计算机技术—计算机系统结构]