XLPE cable partial discharge defect type identification method

Abstract

The invention discloses an XLPE cable partial discharge defect type identification method. The method comprises the following steps: obtaining the de-noised XLPE cable ultrahigh frequency PRPS graph; extracting and computing four dimension statistical characteristic parameters and six non-dimension characteristic parameters of the PRPS graph; inputting the characteristic parameters to SOM neural network model; determining the win nerve cell, continuously learning through updating the weight vector of the win nerve cell by the SOM neural network, until the input sample corresponding to the output layer win nerve cell is stable; and outputting the type identification result of the SOM neural network. The XLPE cable partial discharge defect type identification method is capable of overcoming the defects of the traditional method that the convergence speed is slow and the judgment accuracy is bad, satisfying the visual requirement of the fault analysis, and improving the intelligent level of the XLPE cable partial discharge detection system, has the characters of rapid detection speed, simple process and high diagnosis accuracy, and is good for accurately evaluating the running state of the XLPE cable by the maintenance worker.

Description

technical field [0001] The invention relates to the technical field of XLPE cable insulation defect detection, in particular to a type identification method for XLPE cable partial discharge defects. Background technique [0002] In the urban power transmission system, cross-linked polyethylene (XLPE) power cables have become the mainstream equipment for power transmission. Some XLPE cables currently in operation are about to reach the end of their service life, and their insulation problems are becoming more and more significant. At the same time, the harsh laying environment and local defects of the cable itself greatly shorten the life of the cable, resulting in serious insulation aging and frequent line faults. As an important parameter reflecting the insulation state of cable equipment, partial discharge (hereinafter referred to as PD) is closely related to its insulation state. Effective pattern recognition of PD signals can accurately understand and grasp the nature ...

AI Technical Summary

Problems solved by technology

At the same time, the harsh laying environment and local defects of the cable itself greatly shorten the cable life, resulting in serious insulation aging and frequent line faults

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