Power transformer online detection system based on improved convolutional neural network, and method

A convolutional neural network and power transformer technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as complex operating environments, cascading faults in power grids, and large-scale power outages

Active Publication Date: 2019-04-12
NORTHEASTERN UNIV
View PDF5 Cites 52 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since the power transformer is in the key position of the power grid and the operating environment is complicated, once a failure occurs, it will largely cause grid failures, large-scale power outages, etc., and even cause serious accidents such as exp

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
  • Power transformer online detection system based on improved convolutional neural network, and method
  • Power transformer online detection system based on improved convolutional neural network, and method
  • Power transformer online detection system based on improved convolutional neural network, and method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0116] The invention will be further described below in conjunction with the accompanying drawings and specific implementation examples: the present invention proposes an online detection system and method for a power transformer based on an improved convolutional neural network, wherein the online detection system for a power transformer based on an improved convolutional neural network specifically includes: a sensor Group 2, signal amplification device 3, signal acquisition and storage device 4, fault location device 5, such as Figure 5 shown;

[0117] The sensor group 2 is placed on different positions of the tested transformer box 1, the sensor group 2 is connected with the signal amplifying device 3, the signal amplifying device 3 is connected with the signal acquisition and storage device 4, and the signal acquisition and storage device 4 is connected with the fault The positioning device 5 is connected;

[0118] The sensor group 2 is placed on different positions of ...

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 provides a power transformer online detection system based on an improved convolutional neural network, and a method. The online detection system specifically comprises a sensor set, a signal amplification device, a signal collecting and storing device and a fault positioning device; and the method for the power transformer online detection system based on the improved convolutionalneural network comprises the steps of offline training part and online detection part. According to structural characteristics and the vibration principle of a transformer, distribution points of a vibration sensor are determined, and transformer vibration signals are obtained in real time; original signals are subjected to data processing to be converted into a two-dimensional grey-scale map to serve as convolutional neural network input, wherein the two-dimensional grey-scale map is easily identified through the convolutional neural network; and according to the characteristic that noise interference of real-time vibration data of the transformer is large, the structure of a traditional convolutional neural network is improved, the size of a convolution kernel is improved, an improved convolutional neural network framework applied to power transformer fault positioning is built, and reasonability and superiority of the method are verified.

Description

technical field [0001] The invention belongs to the technical field of power transformer fault diagnosis, and in particular relates to an online power transformer detection system and method based on an improved convolutional neural network. Background technique [0002] As the key equipment for power system safety, power transformer is also one of the most expensive and complicated equipment. Since the power transformer is in the key position of the power grid and the operating environment is complicated, once a failure occurs, it will largely cause grid failures, large-scale power outages, etc., and even cause serious accidents such as explosions and fires. The direct or indirect economic loss can be as high as hundreds of millions of RMB. Therefore, whether the power transformer can operate safely, reliably and stably is closely related to the normal production and life of the people. With the increase of power grid capacity and the concept of "smart grid", smart substat...

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
IPC IPC(8): G01M13/00G06N3/04G06N3/08
CPCG06N3/084G01M13/00G06N3/045
Inventor 杨东升张化光秦佳周博文杨珺王智良罗艳红庞永恒汤琪
Owner NORTHEASTERN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products