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Defect Analysis of Secondary Equipment Based on Power Dictionary and Apriori Algorithm

机译:基于权力字典和APRIORI算法的二级设备缺陷分析

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摘要

In order to improve the operation level and maintenance efficiency of the secondary equipment in the power system, based on the historical defect data, starting from the efficiency of data processing, power system need to build electricity dictionary. In the process of describing and processing the defect data, based on the electric power dictionary, the key characteristics of the defect data can be effectively extracted. From the perspective of data mining, this paper use Apriori algorithm to correlate and analyze the defect data, establish a analysis model for the secondary equipment defect data. Take a provincial electric power company’s secondary equipment historical defect data mining as an example, describes the application process and analysis method of Apriori algorithm. The results show that the algorithm can effectively dig out familial defects and find the weakness of the equipment, it has a certain guiding role for the improvement of equipment performance and secondary equipment operation, maintenance and overhaul.
机译:为了提高电力系统中二级设备的操作水平和维护效率,基于历史缺陷数据,从数据处理的效率开始,电力系统需要建立电信号。在描述和处理缺陷数据的过程中,基于电力词典,可以有效地提取缺陷数据的关键特性。从数据挖掘的角度来看,本文使用APRiori算法来关联和分析缺陷数据,建立辅助设备缺陷数据的分析模型。乘坐省级电力公司的次要设备历史缺陷数据挖掘为例,描述了APRiori算法的应用过程和分析方法。结果表明,该算法可以有效地挖掘家族性缺陷并找到设备的弱点,为改善设备性能和二级设备运行,维护和大修具有一定的指导作用。

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