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投稿时间：2012-04-28
投稿时间：2012-04-28
中文摘要: 为了提高变压器故障诊断的准确性,采用智能互补的思想,将粗糙集和贝叶斯网络相结合来进行变压器故障诊断的研究.先将特征气体的比值进行离散化,再利用粗糙集理论进行属性约简,获得最小诊断规则同时利用贝叶斯网络实现概率推理来分析变压器故障.通过变压器故障诊断实例分析,证明了该方法有效、可行,具有较高的准确率.
Abstract:To improve the accuracy of fault diagnosis,the rough set is combined with Bayesian Network according to complementary strategy.This method discretes the ratio of specific gas and uses rough set theory to reduce attribute,obtain the minimal diagnostic rules.At the same time,Bayesian Network is used to realize probability reasoning and analyze the fault of transformer.Finally,the effectiveness and high accuracy rate are validated with the result of practical fault diagnosis examples.
文章编号：20130108 中图分类号： 文献标志码：
基金项目:
| 作者 | 单位 | |
| 李志斌 | 上海电力学院自动化工程学院 | thermal_1i@163.com |
| 陈成优 | 上海电力学院自动化工程学院 |
引用文本：
李志斌,陈成优.基于粗糙集和贝叶斯网络的变压器故障诊断[J].上海电力大学学报,2013,29(1):30-34.
LI Zhibin,CHEN Chengyou.Fault Diagnosis of Power Transformer Based on Rough Set Theory and Bayesian Network[J].Journal of Shanghai University of Electric Power,2013,29(1):30-34.
李志斌,陈成优.基于粗糙集和贝叶斯网络的变压器故障诊断[J].上海电力大学学报,2013,29(1):30-34.
LI Zhibin,CHEN Chengyou.Fault Diagnosis of Power Transformer Based on Rough Set Theory and Bayesian Network[J].Journal of Shanghai University of Electric Power,2013,29(1):30-34.