Deep-learning-based partial discharge defective image diagnosing method and system

Abstract

The invention provides a deep-learning-based partial discharge defective image diagnosing method and system, comprising the following steps: 1) detecting the partial discharge signal of a piece of power equipment; obtaining a partial discharge defective image; 2) creating a partial discharge defective image sample database; extracting the training set and the testing set; 3) creating a deep convolution neural network model; using samples to do deep learning training and testing to obtain the connection weights and bias parameters of the network model; and 4) inputting the partial discharge defective image to be diagnosed into the network model obtained from the step 3; outputting and obtaining the partial discharge defect type of the image. According to the invention, through the utilization of the learning algorithm of the deep learning theory to complete the characteristic extraction task of a partial discharge defective image, it is possible to accurately and effectively identify the defect type of the partial discharge image without the reliance on manual extraction of the characteristic parameters, providing new solutions for insulation state diagnosing of power equipment.

Description

technical field [0001] The invention relates to the field of fault diagnosis of power equipment, in particular to a method and system for image diagnosis of partial discharge defects based on deep learning. Background technique [0002] Partial discharge is an important symptom of insulation failure in power equipment, and it is also the main reason for further deterioration of insulation. Partial discharge will produce a series of physical phenomena and chemical changes such as light, sound, electrical and mechanical vibration inside and around the power equipment. These various physical and chemical changes accompanying partial discharge can provide detection signals for detecting the insulation state of electrical equipment. Different types of partial discharge have different effects on the insulation performance of equipment, and there are differences in different partial discharge processes. The type of partial discharge can be judged by the difference in signal charac...

AI Technical Summary

Problems solved by technology

In the existing partial discharge processing technology, the accuracy of fault diagnosis and recognition based on pictures is low, and it is difficult to solve complex classification problems

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