[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Butler et al., 1997 - Google Patents

Field studies using a neural-net-based approach for fault diagnosis in distribution networks

Butler et al., 1997

Document ID
10189188643038141068
Author
Butler K
Momoh J
Sobajic D
Publication year
Publication venue
IEE Proceedings-Generation, Transmission and Distribution

External Links

Snippet

The paper discusses results of studies performed on a new fault-diagnosis method for distribution systems using acquired field data. The effectiveness of the fault-diagnosis method in distinguishing between faulted conditions and system conditions that appear fault …
Continue reading at digital-library.theiet.org (other versions)

Classifications

    • 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
    • G01R31/02Testing of electric apparatus, lines or components, for short-circuits, discontinuities, leakage of current, or incorrect line connection
    • G01R31/024Arrangements for indicating continuity or short-circuits in electric apparatus or lines, leakage or ground faults
    • 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
    • G01R31/1263Testing 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 solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
    • 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
    • G01R31/08Locating faults in cables, transmission lines, or networks
    • G01R31/081Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/085Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
    • 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
    • G01R31/08Locating faults in cables, transmission lines, or networks
    • G01R31/088Aspects of digital computing

Similar Documents

Publication Publication Date Title
US7069116B2 (en) High impedance fault detection
US11656263B2 (en) Effective feature set-based high impedance fault detection
Cui et al. Hilbert-transform-based transient/intermittent earth fault detection in noneffectively grounded distribution systems
WO2020015277A1 (en) Arc light fault identifying device and method based on panoramic information
Mohamed et al. Artificial neural network based fault diagnostic system for electric power distribution feeders
Mishra et al. FDOST-based fault classification scheme for fixed series compensated transmission system
Chow et al. Recognizing animal-caused faults in power distribution systems using artificial neural networks
CN101027565B (en) Method and device for detecting electric arc phenomenon on at least one electric cable
CN111999591A (en) Method for identifying abnormal state of primary equipment of power distribution network
Butler et al. Field studies using a neural-net-based approach for fault diagnosis in distribution networks
Hamatwi et al. Comparative analysis of high impedance fault detection techniques on distribution networks
Butler et al. A neural net based approach for fault diagnosis in distribution networks
CN113514720B (en) Arc fault identification method for edge side low-voltage alternating current series connection
Sun et al. Transmission line fault diagnosis method based on improved multiple SVM model
Jain et al. Application of artificial neural networks to transmission line faulty phase selection and fault distance location
CN113297786A (en) Low-voltage fault arc sensing method based on semi-supervised machine learning
Abu-Elanien et al. An ANN-based protection technique for MTDC systems with multiple configurations
Sobajic Field studies using a neural-net-based approach for fault diagnosis in distribution networks
Sundaravaradan et al. Wavelet based transmission line fault analysis: a literature survey
Paul et al. ANFIS based single line to ground fault location estimation for transmission lines
Kumar et al. A single ended wavelet based fault classification scheme in transmission line
Brasil et al. Detection of high impedance faults in primary distribution grid using support vector machines classification
Musa et al. Cross-country, evolving, and inter-circuit relaying scheme for double-circuit transmission line
Athamneh et al. Line voltage-based distance relay using a multistage convolutional neural network classifier
Ensina et al. Fault classification in transmission lines with generalization competence