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

A convolutional neural network and power equipment technology, applied in the field of power equipment failure probability prediction method and system, can solve problems such as loss, achieve the effect of improving probability prediction effect and avoiding the effect of manual selection of probability distribution

Active Publication Date: 2019-10-15
SHANGHAI JIAO TONG UNIV +1
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Problems solved by technology

Due to the complex structure of GIS, if there are defects, it will develop into a fault and caus...

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

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Embodiment Construction

[0036] The method and system for predicting the failure rate of power equipment based on the convolutional neural network described in the present invention will be further explained and described in conjunction with the accompanying drawings and specific embodiments in the following description. However, the explanation and description do not constitute the technical solution of the present invention. Improperly qualified.

[0037] Before the power equipment failure rate prediction system based on the convolutional neural network in this embodiment predicts the failure situation of the power equipment, it first needs to be trained, and the training steps include:

[0038] (1) Collect case PRPS map data and case PRPS map data within the second specified time period of electric equipment within the first specified time period before the specified date; the second specified time period is longer than the first specified time period ;

[0039] (2) Preprocessing the collected cas...

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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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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/06G06K9/62
CPCG06Q10/04G06Q10/0635G06Q50/06G06F18/214G06F18/243Y04S10/50
Inventor 宋辉罗林根李喆万晓琪王辉严英杰钱勇盛戈皞
Owner SHANGHAI JIAO TONG UNIV
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