Power equipment failure rate prediction method and system based on convolutional neural network

Abstract

The invention discloses a power equipment failure rate prediction method based on a convolutional neural network, and the method comprises a training step and a prediction step, and the training stepcomprises the steps: (1) collecting a case PRPS map of power equipment; (2) preprocessing the collected case PRPS atlas data; (3) constructing a first convolutional neural network module, and trainingthe first convolutional neural network module to enable the first convolutional neural network module to output a defect type corresponding to the case PRPS spectrum data; (4) constructing a data setof each defect type based on the defect type; (5) respectively constructing respective fault dichotomy sub-modules corresponding to the defect types, wherein each fault dichotomy sub-module is constructed based on a second convolutional neural network module; and training a second convolutional neural network to enable each fault binary classification sub-module to obtain a fault occurrence probability value based on the case PRPS map data, and outputting a judgment whether the power equipment has a fault or not.

Description

technical field

[0001] The invention relates to a fault prediction method and system in an electric power system, in particular to a fault probability prediction method and system for electric equipment. Background technique

[0002] Gas-insulated switchgear (GIS) is a key equipment in the power system, and its risk degree affects the safe operation of the whole system. Due to the complex structure of GIS, if there are defects, it will develop into a fault and cause heavy losses. Therefore, it is necessary to understand the operation of GIS and find defects in time. Partial discharge will occur when GIS has insulation defects. Therefore, the failure rate of equipment can be predicted based on partial discharge detection data, which can be used for further risk assessment. The risk assessment result is the product of the equipment failure probability and the consequences of the failure. Since the consequences can be set according to the actual situation, the equipment failur...

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