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Fault diagnosis of electric railway traction substation with model-based relation guiding algorithm

机译:基于模型关系指导算法的电力铁路牵引变电站故障诊断

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Most diagnosis systems used in electric railway traction substation are expert systems based on experience, which cannot diagnose faults beyond experience and are very difficult for transplantation and maintenance. In this paper, the model-based diagnosis (MBD) with integration reasoning is firstly used for fault diagnosis of electric railway traction substation. Aiming at the structural characteristics and actual demands of traction substation, we propose a detailed application plan. Rules for element and system models are established. In addition, we propose a relation guiding algorithm (RGA) for searching candidates of minimal conflict set, in which the hidden messages of analytical redundancy relations can be made full use of and the searching space is reduced further. Taking the autotransformer (AT) traction substation in Hefei-Nanjing Passenger Link of China Railway as an example, we construct models for different device objects, and give the descriptions of system model in traction substation. Based on a sequence of fault observations, the fault diagnosis is tested with the proposed plan. The diagnosis result shows that the application plan with MBD and integration reasoning is feasible and effective for traction substation.
机译:在电力铁路牵引变电站中使用的大多数诊断系统都是基于经验的专家系统,这些系统无法根据经验来诊断故障,并且非常难以移植和维护。本文首先将具有集成推理的基于模型的诊断(MBD)用于电力铁路牵引变电站的故障诊断。针对牵引变电站的结构特点和实际需求,提出了详细的应用方案。建立了元素和系统模型的规则。此外,我们提出了一种关系指导算法(RGA),用于搜索最小冲突集的候选对象,其中可以充分利用分析冗余关系的隐藏消息,并进一步减少搜索空间。以中铁合宁客运专线自耦牵引变电站为例,针对不同设备对象构建模型,并给出了牵引变电站系统模型的描述。根据一系列的故障观察结果,使用所提出的计划对故障诊断进行测试。诊断结果表明,结合MBD和综合推理的应用方案对牵引变电站是可行,有效的。

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