Fault characteristic extraction method for switch equipment based on big data platform

A big data platform and switchgear technology, applied in the direction of electrical digital data processing, special data processing applications, testing of mechanical components, etc., can solve problems such as the increase of fault influencing factors, achieve efficient distributed data calculation and analysis, and increase speed , the effect of improving the accuracy

Inactive Publication Date: 2017-10-27
XIDIAN UNIV
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Problems solved by technology

[0006] The technical idea of ​​the present invention is: through the processing of large sample data, the multi-variable and multi-scale entropy MMSE algorithm is introduced to solve the problem of increasing fault influencing fact

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  • Fault characteristic extraction method for switch equipment based on big data platform
  • Fault characteristic extraction method for switch equipment based on big data platform
  • Fault characteristic extraction method for switch equipment based on big data platform

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Embodiment Construction

[0030] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] Traditional feature extraction methods for switchgear faults do not have large-scale data storage and processing capabilities when faced with massive fault influencing factor data, and feature extraction is performed in stand-alone and serial mode, which is slow, inefficient, and poor in security. It directly affects the efficiency and accuracy of fault feature extraction.

[0032] Hadoop big data processing platform, its HDFS distributed file system and MapReduce programming mode can better solve the problem of distributed storage and processing of massive data. Compared with Hadoop, Spark provides the abstraction of distributed data sets, the programming model is more flexible and efficient, and it can make full use of memory to improve performance. Spark can solve iterative and interactive operations very well. It introduces elasti...

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Abstract

The invention provides a fault characteristic extraction method for switch equipment based on a big data platform. According to the fault characteristic extraction method, the problem that the characteristic extraction cannot be performed efficiently and accurately on various types of faults when massive data of switch equipment faults in the prior art are faced is mainly solved. An implementation scheme of the method comprises the steps of establishing a hadoop sub-platform to carry out data collection, storage and pre-processing; establishing a SparkR platform to perform distributed calculation of MMSE (Multivariate Multi-Scale Entropy), and storing a calculation result into an HDFS (Hadoop Distributed File System); downloading the calculation result from the HDFS, and drawing a multivariate sample entropy curve of each fault of the switch equipment by use of R software; and selecting a multivariate sample entropy value in a corresponding scale factor range as a characteristic parameter of each fault according to the multivariate sample entropy curve of each fault. According to the fault characteristic extraction method for the switch equipment based on the big data platform, the whole scheme is designed precisely and completely, the capacities of massive data storage and distributed calculation are achieved, the efficiency and accuracy of the fault characteristic extraction are high, and a basis can be provided for diagnosis and prejudgment of the faults of the switch equipment in time.

Description

technical field [0001] The invention belongs to the technical field of industrial big data processing, and specifically relates to a switchgear fault feature extraction method, which can be applied to feature extraction of various faults of enterprise switchgear. Background technique [0002] As one of the terminal equipment of the power system, the switchgear shoulders the dual tasks of control and protection in the power system. Its reliability and intelligence level will have a profound impact on the stability and automation of the power system. Statistical analysis of switchgear accidents shows that the main causes of high-voltage circuit breaker failures are abnormal operating mechanism, SF6 leakage, damage to auxiliary parts and deterioration of main parts. The influencing factors of switchgear failure mainly include the service time of switchgear, annual load rate, environmental operation level, temperature, operation times and current times, etc. By extracting the f...

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Application Information

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IPC IPC(8): G06F17/30G01R31/327G01M13/00
CPCG06F16/24532G01M13/00G01R31/3275G06F16/172G06F16/176G06F16/182G06F16/2471G06F16/248G06F16/258
Inventor 孔宪光常建涛王佩刘燕龙殷磊
Owner XIDIAN UNIV
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