Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A transformer fault diagnosis method based on vibration noise and a BP neural network

A BP neural network, transformer fault technology, applied in the field of transformer fault processing, can solve problems such as difficulty in transformer fault diagnosis, and achieve the effect of improving accuracy

Active Publication Date: 2018-12-18
ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
View PDF8 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a method to solve the existing shortcomings of difficult diagnosis of transformer faults through vibration signals of transformer state information

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A transformer fault diagnosis method based on vibration noise and a BP neural network
  • A transformer fault diagnosis method based on vibration noise and a BP neural network
  • A transformer fault diagnosis method based on vibration noise and a BP neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0088] Embodiment 2, the vibration signal measurement module uses an acceleration sensor to measure 100 sets of original vibration signals of the transformer, 30 sets of vibration signals of iron core faults, 30 sets of vibration signals of winding faults, and 40 sets of vibration signals of no faults. Through the transformer fault diagnosis method based on vibration noise and BP neural network, the architecture of BP neural network is 5-8-3, that is, there are 5 neurons in the input layer, 3 neurons in the output layer, and 8 neurons in the hidden layer. Neurons.

[0089] S35, according to the number of neurons in the input layer being m, the number of neurons in the output layer n, the number of neurons in the hidden layer of the BP neural network h, and the weight ω between the neuron i in the input layer and the neuron j in the hidden layer ij And the weight ω between hidden layer neuron j and output layer neuron k jk Construct the initial BP neural network;

[0090] Amo...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a transformer fault diagnosis method based on vibration noise and a BP neural network. The invention relates to the technical field of transformer fault treatment, the method comprising the following steps: S1, acquiring the vibration noise sound pressure signals of each area of the transformer through a noise source identification module, and obtaining the area where the maximum noise source is located according to the vibration noise sound pressure signals; S2, acquiring a vibration signal through a vibration signal measuring module to the area where the maximum noisesource is located; S3, adopting BP neural network algorithm to carry out transformer fault diagnosis on the vibration signal. The invention collects the noise sound pressure signal of the transformerthrough S1 and S2, and combines the vibration signal with the fault diagnosis of the transformer through the BP neural network algorithm, thereby greatly improving the accuracy of the fault diagnosisand solving the defect that the fault diagnosis of the transformer is difficult through the vibration signal of the state information of the transformer.

Description

technical field [0001] The invention belongs to the technical field of transformer fault processing, in particular to a transformer fault diagnosis method based on vibration noise and BP neural network. Background technique [0002] The power transformer is one of the most important equipment in the power system. Once the transformer fails, it will have a huge impact on the power grid. Therefore, the maintenance and testing of transformers is very important. However, the structure of the transformer is complex, and the maintenance workload is heavy and difficult. Moreover, frequent disassembly and assembly of the transformer is also easy to damage the components and reduce the reliability of the transformer operation. Live detection fault diagnosis technique is particularly urgent for transformer fault diagnosis. Among the transformer fault diagnosis methods, the vibration method is a reliable transformer fault diagnosis technology that has developed rapidly in recent yea...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/08
CPCG06N3/084G06F30/20
Inventor 黎大健余长厅陈梁远张玉波张磊赵坚颜海俊
Owner ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com