An XLPE power cable partial discharge type identification method

Abstract

The invention discloses an XLPE power cable partial discharge type identification method, and the method comprises the following steps: (1) building an XLPE power cable partial discharge experiment platform, and designing a typical insulation fault partial discharge model; (2) collecting PRPD spectrograms and pulse oscillograms of different insulation faults by utilizing a high-frequency current method, dividing the collected data into a training sample and a test sample, respectively extracting a statistical characteristic quantity from the PRPD spectrograms, extracting a time domain characteristic quantity from an original pulse signal oscillogram, and extracting a frequency domain characteristic quantity from the oscillogram after fast Fourier transform; (3) normalizing the characteristic quantity, setting network parameters by using the training sample, and constructing a fusion extreme learning machine network; And (4) sending the normalized characteristic quantity of the test sample into a trained fusion extreme learning machine network to obtain an identification result of the discharge type. The XLPE power cable partial discharge type identification method can improve the accuracy and stability of XLPE power cable partial discharge type identification.

Description

technical field [0001] The invention relates to the technical field of partial discharge of power distribution equipment, in particular to a method for identifying the type of partial discharge of an XLPE power cable. Background technique [0002] With the rapid development of urban distribution network, XLPE power cables have been widely used, but due to the complexity of manufacturing process, laying environment and operating conditions, XLPE cable insulation defects occur from time to time, if not found and dealt with as early as possible, it will be It develops into an insulation breakdown accident, causing adverse effects such as power outages on the line. Partial discharge detection is an effective live detection method, which can detect the insulation fault of the equipment in time. However, there are various forms of defects, corresponding to different discharge characteristics, and the degree of damage to equipment insulation and the impact on safe operation are al...

AI Technical Summary

Problems solved by technology

[0003] At present, most of the XLPE cable partial discharge type identification methods are traditional multi-hidden layer neural networks, which have slow convergence speed, long training time, and are easy to fall into local optimum.

At the same time, the extraction dimension of discharge features is relatively single, which is prone to misclassification.

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