Abstract
With the increase of the operating time of sulphur hexafluoride (SF6) electrical equipment, the different degrees of discharge may occur inside the equipment. It makes the insulation performance of the equipment decline and will cause serious damage to the equipment. Therefore, it is of practical significance to diagnose fault and assess state for SF6 electrical equipment. In recent years, the frequency of monitoring data acquisition for SF6 electrical equipment has been continuously improved and the scope of collection has been continuously expanded, which makes massive data accumulated in the substation database. In order to quickly process massive SF6 electrical equipment condition monitoring data, we built a two-level fault diagnosis model for SF6 electrical equipment on the Hadoop platform. And we use the MapReduce framework to achieve the parallelization of the fault diagnosis algorithm, which further improves the speed of fault diagnosis for SF6 electrical equipment.
Highlights
SF6 electrical equipment refers to electrical equipment that uses sulphur hexafluoride (SF6) as insulation or arc extinguishing
The internal discharge of SF6 electrical equipment leads to the decomposition of internal SF6 gas molecules
In order to solve the above problems, the two-level fault diagnosis model of SF6 electrical equipment based on the content of SF6 gas derivative as the input for the monitoring data of SF6 electrical equipment was proposed in this paper
Summary
SF6 electrical equipment refers to electrical equipment that uses sulphur hexafluoride (SF6) as insulation or arc extinguishing. The RBF neural network and the decision tree model are used to diagnose the SF6 electrical equipment. This method requires two diagnostics for all data, which will take a lot of time. MapReduce can realize parallel processing of data and greatly improve the processing efficiency of monitoring data of SF6 electrical equipment [13]. In order to solve the above problems, the two-level fault diagnosis model of SF6 electrical equipment based on the content of SF6 gas derivative as the input for the monitoring data of SF6 electrical equipment was proposed in this paper. In order to quickly realize the fault diagnosis of SF6 electrical equipment for the massive SF6 electrical equipment condition monitoring data, this paper implements the fault diagnosis of SF6 electrical equipment on Hadoop platform and realizes the parallelization of SF6 electrical equipment fault diagnosis algorithm
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