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

Chen et al., 2023 - Google Patents

Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing

Chen et al., 2023

View PDF
Document ID
14964983534437790857
Author
Chen Z
Yang L
Tian J
Chen Z
Xu X
Zhao E
Publication year
Publication venue
Distributed Generation & Alternative Energy Journal

External Links

Snippet

In the face of the challenges brought by the complexity of power grid, diversification of disturbance factors, isolation of monitoring points and other issues to the cause identification of voltage sag disturbance, this paper proposes a real-time monitoring technology for …
Continue reading at journals.riverpublishers.com (PDF) (other versions)

Classifications

    • 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
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00Communication or information technology specific aspects supporting electrical power generation, transmission, distribution or end-user application management
    • Y04S40/20Information technology specific aspects
    • Y04S40/22Computer aided design [CAD]; Simulation; Modelling
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GASES [GHG] EMISSION, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/70Systems integrating technologies related to power network operation and communication or information technologies mediating in the improvement of the carbon footprint of electrical power generation, transmission or distribution, i.e. smart grids as enabling technology in the energy generation sector not used, see subgroups
    • Y02E60/76Computer aided design [CAD]; Simulation; Modelling
    • 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
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/54Management of operational aspects, e.g. planning, load or production forecast, maintenance, construction, extension
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Wang et al. A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network
Yadav et al. Real-time event classification in power system with renewables using kernel density estimation and deep neural network
Li et al. Transient stability assessment of power system based on XGBoost and factorization machine
CN109635928A (en) A kind of voltage sag reason recognition methods based on deep learning Model Fusion
Yu et al. Fault location in distribution system using convolutional neural network based on domain transformation
Cao et al. Multi-step wind power forecasting model using LSTM networks, similar time series and LightGBM
CN106909989A (en) A kind of grid disturbance Forecasting Methodology and device
Shi et al. Online event detection in synchrophasor data with graph signal processing
Rizvi et al. Data-driven short-term voltage stability assessment using convolutional neural networks considering data anomalies and localization
Wang et al. Online analysis of voltage security in a microgrid using convolutional neural networks
CN113937764A (en) Low-voltage distribution network high-frequency measurement data processing and topology identification method
Chang Comparison of three short term wind power forecasting methods
CN115275990A (en) Evaluation method and system for broadband oscillation risk of regional power grid
Gong et al. Transient stability assessment of electric power system based on voltage phasor and cnn-lstm
Jiang et al. Application of a hybrid model of big data and BP network on fault diagnosis strategy for microgrid
Du et al. A hierarchical power system transient stability assessment method considering sample imbalance
Chen et al. Real-time Monitoring Technology of Voltage Sag Disturbance in Distribution Network Based on TCN-Attention Neural Network and Flink Flow Computing
Dabou et al. Supervised learning of overcomplete dictionaries for rapid response-based dynamic stability prediction
He et al. A method for transient stability assessment based on pattern recognition
Chen et al. 2 Analysis of Voltage Sag Characteristics
Khaledian et al. Event-Based Dynamic Response Modeling of Large Behind-the-Meter Solar Farms: A Data-Driven Method Based on Real-World Data
Li et al. Residential Photovoltaic Power Forecasting Considering Division of Weather Type Index Interval
Wang et al. Research Progress on the Application of Machine Learning in Power System Security
Wu et al. Faulty Line Identification in AC–DC Hybrid Grids Based on MTF and Improved Resnet
Chen et al. Icing Load and Risk Forecasting for Power Transmission Line Based on Multiscale Time Series Phase-Space Reconstruction and Regression