Amoateng et al., 2021 - Google Patents

Topology detection in power distribution networks: A PMU based deep learning approach

Amoateng et al., 2021

Document ID
8106847808061960042
Author
Amoateng D
Yan R
Mosadeghy M
Saha T
Publication year
Publication venue
IEEE Transactions on Power Systems

External Links

Snippet

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/06Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation

Similar Documents

Publication Publication Date Title
James et al. Intelligent time-adaptive transient stability assessment system
Li et al. Real-time faulted line localization and PMU placement in power systems through convolutional neural networks
Zhou et al. Partial knowledge data-driven event detection for power distribution networks
Zhao et al. Full-scale distribution system topology identification using Markov random field
Zhang et al. Post‐disturbance transient stability assessment of power systems by a self‐adaptive intelligent system
Mohammadi et al. A fast fault detection and identification approach in power distribution systems
Ferreira et al. A survey on intelligent system application to fault diagnosis in electric power system transmission lines
Xu et al. A reliable intelligent system for real-time dynamic security assessment of power systems
Amoateng et al. Topology detection in power distribution networks: A PMU based deep learning approach
Mohammadi et al. PMU based voltage security assessment of power systems exploiting principal component analysis and decision trees
Cremer et al. A machine-learning based probabilistic perspective on dynamic security assessment
Manohar et al. Microgrid protection under wind speed intermittency using extreme learning machine
Hassani et al. Unsupervised concrete feature selection based on mutual information for diagnosing faults and cyber-attacks in power systems
Manohar et al. Enhancing resilience of PV-fed microgrid by improved relaying and differentiating between inverter faults and distribution line faults
Zhao et al. Robust PCA-deep belief network surrogate model for distribution system topology identification with DERs
Zhu et al. Networked time series shapelet learning for power system transient stability assessment
Nguyen et al. Spatial-temporal recurrent graph neural networks for fault diagnostics in power distribution systems
Mirzaei et al. Comparative analysis of probabilistic neural network, radial basis function, and feed-forward neural network for fault classification in power distribution systems
Mukherjee et al. Real-time dynamic security analysis of power systems using strategic PMU measurements and decision tree classification
Liang et al. Power flow matching-based topology identification of medium-voltage distribution networks via AMI measurements
Thirugnanasambandam et al. AdaBoost classifiers for phasor measurements‐based security assessment of power systems
Zhao et al. Efficient neural network architecture for topology identification in smart grid
Chen et al. Real‐time recognition of power quality disturbance‐based deep belief network using embedded parallel computing platform
Francis et al. Topology identification of power distribution systems using time series of voltage measurements
Kurup et al. Ensemble models for circuit topology estimation, fault detection and classification in distribution systems