计算机应用与软件2017，Vol.34Issue(2)：93-99,105,8.DOI:10.3969/j.issn.1000-386x.2017.02.016
基于变精度粗糙集与量子贝叶斯网络的变压器故障诊断研究
RESEARCH ON TRANSFORMER FAULT DIAGNOSIS BASED ON VARIABLE PRECISION ROUGH SET AND QUANTUM BAYESIAN NETWORK
摘要
Abstract
It is significance for electric power department to diagnose transformer fault timely and accurately.Aiming at the problem that the rough set and the Bayesian network model have great influence on the transformer fault diagnosis and the complete search NP difficulty,a transformer fault diagnosis model based on variable precision rough set and quantum Bayesian network is proposed.By using Grover quantum search algorithm,the target data such as transformer fault and symptom type can be searched quickly.The analytic hierarchy process is used to delete the index which has less influence on the diagnosis fault and to analyze the misclassification rate of the variable precision rough set.The minimum fault decision table is obtained,and the fault reasoning model of Bayesian network is constructed to diagnose the transformer fault.Example analysis shows that compared with rough sets and quantum Bayesian network model,the proposed model is more suitable for the transformer fault diagnosis and more accurate diagnosis.关键词
变压器故障诊断/变精度粗糙集/Grover量子搜索算法/贝叶斯网络Key words
Transformer fault diagnosis/Variable precision rough set/Grover quantum search algorithm/Bayesian network分类
信息技术与安全科学引用本文复制引用
郭栋,熊文真,徐建新,韩继光,李哲..基于变精度粗糙集与量子贝叶斯网络的变压器故障诊断研究[J].计算机应用与软件,2017,34(2):93-99,105,8.基金项目
国家自然科学基金青年科学基金项目(51406071). （51406071）
