• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Machinery, Materials and Information Technology Applications >Fault Diagnosis for Power Grid Systems Based on Rough Set and Bayesian Network
【24h】

Fault Diagnosis for Power Grid Systems Based on Rough Set and Bayesian Network

机译：基于粗糙集和贝叶斯网络的电网系统故障诊断

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In terms of the uncertainties and incompleteness of alarm information in power grid fault diagnosis, this paper proposes a fault diagnosis method based on rough set combined with Bayesian network. Using the ability of rough set to reduce knowledge and process indeterminate information and mine fault information hierarchically, using the attribute reducing method based on cognizable matrix and information entropy, the optimal attribute reduction combination is extracted. Finally, by means of the reduction decision table formed by optimal attribute reduction combination, the Bayesian network model is built for parallel reasoning of each region, and the nodal probability is trained to achieve fault diagnosis. The experiment proves that this method can diagnose the fault rapidly and accurately, and has strong fault tolerance and adaptability.
机译：在电网故障诊断中报警信息的不确定性和不完整性方面，本文提出了一种基于粗糙集合与贝叶斯网络相结合的故障诊断方法。 利用粗糙集的能力在层次的基础上分层地减少知识和过程不确定信息和矿井故障信息，提取最佳属性缩减方法。 最后，借助于通过最佳属性减少组合形成的还原判定表，贝叶斯网络模型建立了每个区域的并行推理，并且训练了节点概率以实现故障诊断。 实验证明，该方法可以快速准确地诊断故障，具有强大的容错和适应性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号