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基于故障群组合优化的变电站故障诊断

A Fault Diagnosis System for Substation Based on Optimizing Combinations of Fault-Masses

【作者】 马忠坤

【导师】 张炳达;

【作者基本信息】 天津大学 , 电力系统及其自动化, 2004, 硕士

【摘要】 在变电站发生故障后,利用故障诊断系统尽快诊断出故障位置和故障类型,对于减少停电损失有着重要意义。目前故障诊断领域面临的主要问题就是故障信息的两种不确定性问题:一种是故障信息中保护动作和断路器跳闸的可靠性,另一种是收到的警报信息的正确性和末收到的警报信息实际出现的可能性。针对这个问题本文提出了基于故障群组合优化的变电站故障诊断方法。将变电站中保护装置或断路器的拒动看作一种故障,由一个故障源和若干个与其相关的保护装置拒动、断路器拒动组成的整体称为故障群,把故障群在当前变电站运行方式下应产生的故障征兆称为征兆群,每一对故障群和征兆群对应一个故障停电区域。采用节点区域法对变电站进行区域划分,各个节点区域间用支路相连,将变电站主接线图转化成了便于故障设置、故障分析的变电站网络结构图,并创建了各种保护模型。以它们为工作平台分析故障设置下的故障征兆。变电站故障诊断系统以有效故障群作为诊断空间的基本单位,即只考虑与当前故障有关的故障群,将故障诊断问题转化为故障群—征兆群对子组合和已知故障征兆间的匹配问题。创建故障群组合作为已知故障征兆诊断解的适应度函数,用遗传算法搜索出最优诊断解。本文提出的变电站故障诊断系统能够辨识出故障源、保护装置拒动、断路器装置拒动及信号传输错误,较好地解决了两种不确定性问题。对现场人员迅速确认故障设备非常有利。

【Abstract】 It is important for power system restoration to identify the fault section, whenfault occurs. The task becomes challenging because of some protective devicenon-operations or circuit breaker non-operations and some faults of sensors andcommunication equipment. A new method for fault diagnosis based on optimizing thecombination of fault-masses is presented in this paper. Protective device non-operations and circuit breaker non-operations are thoughtas faults. One group made up of a fault source and some related protective devicenon-operations or circuit breaker non-operations is called fault-mass; and a series ofsymptoms resulted from a fault-mass is called symptom-mass. Each fault-mass iscorresponding with a symptom-mass and a fault black-out field. The entire field ofsubstation is plotted into many fields represented by nodes, which are connected withothers by branches, and all protections are modeled by the protection model. Faultsare set and analyzed based on this substation network, so fault symptoms of the faultsare acquired. The concept of effective fault-mass is presented with the help of the known faultblack-out fields and fault symptoms, and the fault diagnosis problem is transformedinto matching the known fault symptoms with some effective fault-masses. Candidateanswers are evaluated by the fitness function, and the genetic algorithm is adopted tosearch out the optimal answer from the combinations of effective fault-masses. The developed methodology has the ability to deal with complicated faults, andthe diagnosis results include not only fault sources, but also device non-operationsand missing signals. It is beneficial for the operators to confirm broken-downcomponents quickly.

  • 【网络出版投稿人】 天津大学
  • 【网络出版年期】2004年 04期
  • 【分类号】TM76
  • 【下载频次】214
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