KR100729107B1 - 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법 - Google Patents

부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법 Download PDF

Info

Publication number
KR100729107B1
KR100729107B1 KR1020050101969A KR20050101969A KR100729107B1 KR 100729107 B1 KR100729107 B1 KR 100729107B1 KR 1020050101969 A KR1020050101969 A KR 1020050101969A KR 20050101969 A KR20050101969 A KR 20050101969A KR 100729107 B1 KR100729107 B1 KR 100729107B1
Authority
KR
South Korea
Prior art keywords
phase
neural network
partial discharge
input vector
discharge
Prior art date
Application number
KR1020050101969A
Other languages
English (en)
Korean (ko)
Other versions
KR20070045558A (ko
Inventor
구선근
박기준
윤진열
곽주식
Original Assignee
한국전력공사
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 한국전력공사 filed Critical 한국전력공사
Priority to KR1020050101969A priority Critical patent/KR100729107B1/ko
Priority to GB0600677A priority patent/GB2431726B/en
Priority to JP2006027771A priority patent/JP4244353B2/ja
Priority to DE102006008482A priority patent/DE102006008482B4/de
Publication of KR20070045558A publication Critical patent/KR20070045558A/ko
Application granted granted Critical
Publication of KR100729107B1 publication Critical patent/KR100729107B1/ko

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G01R31/1254Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/14Circuits therefor, e.g. for generating test voltages, sensing circuits

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Testing Relating To Insulation (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
KR1020050101969A 2005-10-27 2005-10-27 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법 KR100729107B1 (ko)

Priority Applications (4)

Application Number Priority Date Filing Date Title
KR1020050101969A KR100729107B1 (ko) 2005-10-27 2005-10-27 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법
GB0600677A GB2431726B (en) 2005-10-27 2006-01-13 Input vector formation method of neural networks for auto-identification of partial discharge source
JP2006027771A JP4244353B2 (ja) 2005-10-27 2006-02-03 部分放電原因自動推論用の神経網エンジンの入力ベクトル生成方法
DE102006008482A DE102006008482B4 (de) 2005-10-27 2006-02-23 Eingangsvektorbildungsverfahren bei neuronalen Netzwerken zur Auto-Identifikation einer partiellen Entladungsquelle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020050101969A KR100729107B1 (ko) 2005-10-27 2005-10-27 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법

Publications (2)

Publication Number Publication Date
KR20070045558A KR20070045558A (ko) 2007-05-02
KR100729107B1 true KR100729107B1 (ko) 2007-06-14

Family

ID=37912931

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020050101969A KR100729107B1 (ko) 2005-10-27 2005-10-27 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법

Country Status (4)

Country Link
JP (1) JP4244353B2 (ja)
KR (1) KR100729107B1 (ja)
DE (1) DE102006008482B4 (ja)
GB (1) GB2431726B (ja)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200015300A (ko) 2018-08-03 2020-02-12 국민대학교산학협력단 신경망 피처 벡터 결정 장치 및 방법
KR20210025246A (ko) 2019-08-27 2021-03-09 국민대학교산학협력단 가중치 기반의 피처 벡터 생성 장치 및 방법
KR20210035502A (ko) 2019-09-24 2021-04-01 국민대학교산학협력단 보안관제 데이터 분석을 위한 머신러닝 기반의 학습 벡터 생성 장치 및 방법
KR20210043932A (ko) 2019-10-14 2021-04-22 국민대학교산학협력단 라벨 정보가 포함된 특징 벡터 생성 장치 및 방법
KR20230165650A (ko) 2022-05-27 2023-12-05 국민대학교산학협력단 그래프 랜덤워크 기반 보안관제 이벤트 분석 장치 및 방법

Families Citing this family (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100853725B1 (ko) * 2007-06-20 2008-08-22 (주) 피에스디테크 Prps 알고리즘을 이용한 가스절연부하개폐장치의부분방전 원인분석 방법 및 그 장치
KR100919293B1 (ko) * 2008-09-24 2009-10-01 주식회사 케이디파워 전기 시스템의 직렬아크 검출장치 및 방법
KR101051099B1 (ko) * 2008-09-30 2011-07-21 한국전력공사 고전압 전력 기기의 극 초단파 부분 방전 및 방전위치 측정장치
CN102221651B (zh) * 2011-03-11 2015-05-27 太原理工大学 一种矿用隔爆型干式变压器故障在线诊断及预警方法
US20120330871A1 (en) * 2011-06-27 2012-12-27 Asiri Yahya Ahmed Using values of prpd envelope to classify single and multiple partial discharge (pd) defects in hv equipment
FR2981457B1 (fr) * 2011-10-12 2015-04-03 Michel Gaeta Procede et dispositif de detection d'un dysfonctionnement dans un reseau electrique
CN104391184B (zh) * 2014-11-12 2017-04-12 珠海市齐飞信息技术有限公司 台变缺相检测和示警装置及方法
CN105334436B (zh) * 2015-10-30 2018-08-10 山东电力研究院 基于som-bp组合神经网络的交联电缆局部放电模式识别方法
CN105606976B (zh) * 2016-03-09 2018-09-18 国家电网公司 一种用于gis局部放电特高频在线监测的抗干扰方法
JP6553303B2 (ja) * 2016-09-01 2019-07-31 株式会社東芝 部分放電監視システム
WO2019075612A1 (en) 2017-10-16 2019-04-25 Abb Schweiz Ag CIRCUIT BREAKER MONITORING METHOD AND APPARATUS AND INTERNET OF OBJECTS USING THE SAME
KR102051670B1 (ko) 2017-12-19 2019-12-04 한국전력공사 전력설비 기자재 불량 검출 장치, 시스템 및 방법이 기록된 컴퓨터 판독가능 기록 매체
CN108896857B (zh) * 2018-07-06 2020-12-01 北京四方继保自动化股份有限公司 一种基于深度学习的变压器复杂工况识别方法
KR102089187B1 (ko) * 2018-12-11 2020-03-13 한국전력공사 전력케이블 접속함 진단 시스템 및 방법
CN110096723B (zh) * 2019-01-22 2023-05-16 国网山西省电力公司长治供电公司 基于运维检测大数据的高压开关柜绝缘状态分析方法
CN110286303A (zh) * 2019-07-10 2019-09-27 国家电网有限公司 一种基于bp神经网络的同轴电缆绝缘老化状态评估方法
CN111707915B (zh) * 2020-07-10 2022-10-14 中车唐山机车车辆有限公司 基于牵引电机局部放电的在线绝缘评估方法及装置
KR102607227B1 (ko) * 2020-09-15 2023-11-29 한국전력공사 센서 데이터 활용 부분 방전 감지 시스템 및 방법
CN115453286B (zh) * 2022-09-01 2023-05-05 珠海市伊特高科技有限公司 Gis局部放电诊断方法、模型训练方法、装置及系统

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR200487036Y1 (ko) * 2017-09-22 2018-08-28 동서위생 주식회사 화장 솜

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5260871A (en) * 1991-07-31 1993-11-09 Mayo Foundation For Medical Education And Research Method and apparatus for diagnosis of breast tumors
DE4333259C1 (de) * 1993-09-27 1995-05-24 Siemens Ag Verfahren zum Erzeugen eines die Richtung eines Kurzschlußstromes angebenden Richtungssignals
JP3201959B2 (ja) * 1996-09-03 2001-08-27 古河電気工業株式会社 部分放電測定方法
US6650779B2 (en) * 1999-03-26 2003-11-18 Georgia Tech Research Corp. Method and apparatus for analyzing an image to detect and identify patterns
GB0104763D0 (en) * 2001-02-27 2001-04-18 Smiths Group Plc Arc detection

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR200487036Y1 (ko) * 2017-09-22 2018-08-28 동서위생 주식회사 화장 솜

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200015300A (ko) 2018-08-03 2020-02-12 국민대학교산학협력단 신경망 피처 벡터 결정 장치 및 방법
KR20210025246A (ko) 2019-08-27 2021-03-09 국민대학교산학협력단 가중치 기반의 피처 벡터 생성 장치 및 방법
KR20210035502A (ko) 2019-09-24 2021-04-01 국민대학교산학협력단 보안관제 데이터 분석을 위한 머신러닝 기반의 학습 벡터 생성 장치 및 방법
KR20210043932A (ko) 2019-10-14 2021-04-22 국민대학교산학협력단 라벨 정보가 포함된 특징 벡터 생성 장치 및 방법
KR20230165650A (ko) 2022-05-27 2023-12-05 국민대학교산학협력단 그래프 랜덤워크 기반 보안관제 이벤트 분석 장치 및 방법

Also Published As

Publication number Publication date
JP4244353B2 (ja) 2009-03-25
GB2431726B (en) 2010-04-21
GB2431726A (en) 2007-05-02
DE102006008482A1 (de) 2007-05-03
DE102006008482B4 (de) 2010-10-07
GB0600677D0 (en) 2006-02-22
KR20070045558A (ko) 2007-05-02
JP2007124880A (ja) 2007-05-17

Similar Documents

Publication Publication Date Title
KR100729107B1 (ko) 부분방전 원인 자동 추론용 신경망 회로의 입력벡터생성방법
Huang et al. Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples
US11092952B2 (en) Plant abnormality detection method and system
Suh et al. Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks
Ghate et al. Cascade neural-network-based fault classifier for three-phase induction motor
US10977568B2 (en) Information processing apparatus, diagnosis method, and program
Rudd et al. A generic knowledge-based approach to the analysis of partial discharge data
EP0572767A2 (en) Monitoring diagnostic apparatus using neural network
Pavlovski et al. Hierarchical convolutional neural networks for event classification on PMU measurements
Aschenbrenner et al. On line PD measurements and diagnosis on power transformers
KR102289212B1 (ko) 인공지능 기반 고장 진단 장치 및 방법
US20220156137A1 (en) Anomaly detection method, anomaly detection apparatus, and program
Ma et al. Fractal‐based autonomous partial discharge pattern recognition method for MV motors
Islam et al. A wavelet approach for precursor pattern detection in time series
Thi et al. Anomaly detection for partial discharge in gas-insulated switchgears using autoencoder
CN113971651A (zh) 检测方法以及检测装置、电子设备和存储介质
Dwiputranto et al. Machinery equipment early fault detection using Artificial Neural Network based Autoencoder
Yang et al. Brown measure based spectral distribution analysis for spatial-temporal localization of cascading events in power grids
Mukhopadhyay et al. Fault detection in sensors using single and multi-channel weighted convolutional neural networks
KR20210059322A (ko) 부분방전 위치 추정 장치 및 방법
CN110320802A (zh) 基于数据可视化的复杂系统信号时序识别方法
Tosyali et al. Data-driven gantry health monitoring and process status identification based on texture extraction
KR101137318B1 (ko) 반도체 장치의 고장 진단시스템 및 방법
KR20070104073A (ko) 반도체 제조 설비의 공정 분석 시스템 및 그의 분석 방법
Diefenthäler et al. Artificial neural networks: Modeling and comparison to detect high impedance faults

Legal Events

Date Code Title Description
A201 Request for examination
E902 Notification of reason for refusal
E701 Decision to grant or registration of patent right
GRNT Written decision to grant
FPAY Annual fee payment

Payment date: 20130603

Year of fee payment: 7

FPAY Annual fee payment

Payment date: 20140602

Year of fee payment: 8

FPAY Annual fee payment

Payment date: 20150601

Year of fee payment: 9

FPAY Annual fee payment

Payment date: 20160601

Year of fee payment: 10

FPAY Annual fee payment

Payment date: 20170601

Year of fee payment: 11

FPAY Annual fee payment

Payment date: 20180530

Year of fee payment: 12

FPAY Annual fee payment

Payment date: 20190529

Year of fee payment: 13

R401 Registration of restoration