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侯权, 赵岭忠, 熊远武. 基于粗糙集与ASP的变压器故障诊断[J]. 桂林电子科技大学学报, 2017, 37(2): 147-153.
引用本文: 侯权, 赵岭忠, 熊远武. 基于粗糙集与ASP的变压器故障诊断[J]. 桂林电子科技大学学报, 2017, 37(2): 147-153.
HOU Quan, ZHAO Lingzhong, XIONG Yuanwu. Transformer fault diagnosis based on rough set theory and ASP[J]. Journal of Guilin University of Electronic Technology, 2017, 37(2): 147-153.
Citation: HOU Quan, ZHAO Lingzhong, XIONG Yuanwu. Transformer fault diagnosis based on rough set theory and ASP[J]. Journal of Guilin University of Electronic Technology, 2017, 37(2): 147-153.

基于粗糙集与ASP的变压器故障诊断

Transformer fault diagnosis based on rough set theory and ASP

  • 摘要: 针对传统电力变压器故障诊断方法无法获取完备故障信息、加入新约束条件需重新构建系统模型,提出一种基于粗糙集理论和ASP的电力变压器故障诊断方法。利用粗糙集理论对变压器油溶解的气体进行分析,结合ASP规则将待求解的问题转化为ASP知识库,通过ASP求解器实现故障诊断。与传统方法相比,该诊断技术简洁精确,模型表达能力强,具有一定的灵活性和容错能力,可以实现知识库的动态维护,变压器故障诊断准确率可达94.8%。

     

    Abstract: For the traditional fault diagnosis method of power transformers cannot effectively obtain complete fault information, and thesystem model must be re-built when new constraints were added,a new fault diagnosis method of power transformer based on rough set theory and ASP was proposed.The gas dissolved in transformer oil is analyzed by rough set theory, then the problems to be solved are converted into ASP knowledge base by combining with ASP rules, and fault diagnosis are realized by the ASP solver. Compared with traditional systems, the diagnostic technique is simple and accurate, its expression ability is strong and has a certain flexibility and fault-tolerant ability, the dynamic maintenance of the knowledge base can be realized, and the accuracy rate of transformer fault diagnosis can be up to 94.8%.

     

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