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

A Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network

A long-short-term memory and recognition switch technology, which is applied to pattern recognition in signals, character and pattern recognition, instruments, etc., can solve the problems of single signal acquisition, unsatisfactory diagnosis results, and low efficiency of recognition models, and achieve high recognition ability , broaden the channels of information collection, and comprehensively and accurately reflect the effect of

Active Publication Date: 2020-05-19
NANJING KANGNI RING NETWORK SWITCH EQUIP CO LTD +1
View PDF9 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to overcome the problems existing in the existing switchgear fault detection, and invented a method based on the audible sound and the ultrasonic signal of the parallel long-short-term memory neural network to identify the fault of the switchgear. The method collects the audible signal and Ultrasonic signals, obtain a large number of characteristic parameters, use the long-short-term memory (LSTM) neural network in the field of deep learning to train and identify characteristic data sets, so as to judge the working status of the switchgear, and solve the problem of single signal acquisition in the sound diagnosis of switchgear faults at this stage , the problem of inefficient identification model and unsatisfactory diagnosis results

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 Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network
  • A Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network
  • A Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] The invention extracts new characteristic ZMSF parameters (1-10) order from the audible sound signal, combines with other characteristic types and calculates the corresponding statistical function to ensure that the obtained data is highly consistent with the collected signal.

[0053] In the invention, a parallel mutual-feeding long-short-term memory (LSTM) neural network model is used as the core algorithm for training and recognition. The long-short-term memory neural network model of the parallel mutual-feedback structure can recognize or monitor audible sound signals and ultrasonic signals at the same time, and the calculation results are fed back to each other, and the calculation results of one side are terminated or strengthened by the way of mutual feedback control. In this way, computing resources are saved and the recognition effect is improved.

[0...

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 method for identifying switchgear faults based on a parallel long-short-term memory neural network, including: (1) collecting and processing audible sound signals and ultrasonic signals; (2) training data sets generated from audible sound signals The training data set generated by the ultrasonic signal and the ultrasonic signal is put into the LSTM network model with a parallel mutual feed structure for training, and the recognition model is obtained; (3) the sensor is used to collect audible sound data and ultrasonic data, and the switch cabinet is monitored online; (4) Put the real-time collected data into the trained LSTM network model to identify and get the prediction results, and judge the switchgear failure. The invention can save computing resources, improve the identification ability, and improve the accuracy of fault judgment of switch cabinets, thereby making the distribution network more efficient and intelligent.

Description

technical field [0001] The invention relates to the technical field of sound signal diagnosis equipment faults, in particular to a method for identifying switch cabinet faults based on a parallel long-short-term memory neural network. Background technique [0002] The operation and maintenance of power equipment has always been a key concern and research issue in the power system. As one of the main equipment in the power transmission and distribution process, the high-voltage switchgear operates safely and ensures the safety and reliability of the power system. . Affected by voltage fluctuations, equipment aging, insulating gas leakage and other reasons, partial discharge of switchgear equipment will cause insulation damage and cause failures. In addition to voltage, current, temperature, flashover and other phenomena in the process, there are also Discharge sound phenomenon, including audible and ultrasonic signals. On-line monitoring of the insulation status of the swit...

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 Patents(China)
IPC IPC(8): G01R31/12G06K9/00G06K9/62
CPCG01R31/12G06F2218/10G06F2218/12G06F18/214G06F18/24
Inventor 史塨毓曹雪虹周喜章王青云戴宁冯月芹
Owner NANJING KANGNI RING NETWORK SWITCH EQUIP 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