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基于故障树分析法—电力变压器故障诊断

The Fault Diagnose of Electric Power Transformer Based on Fault Tree Analysis

【作者】 胡勇

【导师】 王群京;

【作者基本信息】 合肥工业大学 , 电力系统及其自动化, 2002, 硕士

【摘要】 电力变压器是电网运行的主设备,它的安全运行是保障电力系统可靠运行条件之一。为了满足电力需求,电力系统的规模日益扩大、变压器的单机容量也随着增大。因而,其故障不仅对变压器本身造成损失,同时也会因变压器故障而波及电力网络,给用户造成间接损失。加强对电力变压器各种故障现象的监测并做好故障征兆的预测,能有效地防止变压器故障发生,完善电力变压器故障诊断技术是日益重要的课题。 针对变压器内部故障诊断特点,运用故障树分析技术、专家系统方法和模糊数学理论对变压器故障诊断技术进行了研究,主要的成果在于以下几个方面: 1,通过对近年来大型电力变压器故障实例做出分析,用故障树分析法(FAT),建立变压器故障树诊断模型,并把它运用到变压器故障诊断的专家系统中。 2,对电力变压器绝缘油内溶解气体分析法(DGA)进行模糊化处理。运用模糊推理和模糊匹配理论对故障定位,为变压器故障诊断专家系统奠定基础。 3,把模糊理论运用到变压器故障诊断中,对电力变压器故障诊断模型中各种不确定性因素进行模糊化处理的方法,将确定知识的专家系统模糊化为更具有实际意义与实用价值的模糊专家系统。

【Abstract】 Electric Power Transformer is the important electrical equipment of power network,its safe operation premise the safety service of electrical power system. For the power demand of users,the power system scale is rising,so does single transformer capacity. Therefore the fault damages transformer itself,affects the power system network,and brings loss to users. In order to protect from the fault of transformer,we should reinforce the monitor on varies transformer faults and predict transformers’ faults in advance. So the diagnostic technique has been becoming a important question for study or discussion.According to the diagnostic characteristic of the interior fault in transformer,we study the fault diagnostic of transformer by FAT,ES,FT. the main achievement lies in below:1, Constructing the transformer model of diagnostic fault apply in FAT with help of the data of actual transformer fault during several years and applying in transformer fault diagnostic expert system.2, Dissolved gas-in-oil analysis is done fuzzy process. Applying in fuzzy inference and match for locating faults,in order to lay solid foundations for transformers’ faults diagnostic expert system.3,all sorts of uncertainly factors that exits in transformers’ faults are analyzed fuzzy set theory and uncertainly inference stratagem ,and expert system is transformed to fuzzy expert system that is more practical and more significant.

  • 【分类号】TM407
  • 【被引频次】11
  • 【下载频次】1021
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