Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2022 - Google Patents

Time series anomaly detection for smart grids via multiple self-supervised tasks learning

Zhang et al., 2022

Document ID
4813342039425467248
Author
Zhang Z
Zhao L
Cai D
Feng S
Miao J
Guan Y
Tao H
Cao J
Publication year
Publication venue
2022 IEEE International Conference on Knowledge Graph (ICKG)

External Links

Snippet

Smart grids have achieved tremendous success in energy infrastructure and also play an vital role in our daily lives. As an important auxiliary service, anomaly detection in smart grids, eg, detecting abnormal activities and anomalous events from the electricity data, is …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/086—Learning methods using evolutionary programming, e.g. genetic algorithms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • Y—GENERAL 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS 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
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/54—Management of operational aspects, e.g. planning, load or production forecast, maintenance, construction, extension
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Nizam et al. Real-time deep anomaly detection framework for multivariate time-series data in industrial iot
Liu et al. Communication-efficient federated learning for anomaly detection in industrial internet of things
Huang et al. Real-time fault detection for IIoT facilities using GBRBM-based DNN
Du et al. GAN-based anomaly detection for multivariate time series using polluted training set
Liu et al. Searching for critical power system cascading failures with graph convolutional network
CN115688035A (en) Time sequence power data anomaly detection method based on self-supervision learning
Miao et al. Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks
Huang et al. SOPA‐GA‐CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet‐of‐Things
Liao et al. Electricity theft detection using dynamic graph construction and graph attention network
Yan et al. TFDPM: Attack detection for cyber–physical systems with diffusion probabilistic models
Zhang et al. Time series anomaly detection for smart grids via multiple self-supervised tasks learning
Şahin DCW-RNN: Improving class level metrics for software vulnerability detection using artificial immune system with clock-work recurrent neural network
Zhuang et al. Dynamic generative residual graph convolutional neural networks for electricity theft detection
Zhu et al. Auto-starting semisupervised-learning-based identification of synchrophasor data anomalies
Shan et al. Multisensor Anomaly Detection and Interpretable Analysis for Linear Induction Motors
Li et al. Tadl: Fault localization with transformer-based anomaly detection for dynamic microservice systems
Wen et al. A network security situation awareness method based on gru in big data environment
Wang et al. A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders
CN114401135B (en) Internal threat detection method based on LSTM-Attention user and entity behavior analysis technology
Chen et al. Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model
Tao et al. HAN-CAD: hierarchical attention network for context anomaly detection in multivariate time series
Pan et al. A model for detecting false data injection attacks in smart grids based on the method utilized for image coding
Xie et al. Anomaly detection for multivariate time series in IoT using discrete wavelet decomposition and dual graph attention networks
Qin et al. MTAD RF: Multivariate Time-series Anomaly Detection based on Reconstruction and Forecast
Liao et al. DPDGAD: A Dual-Process Dynamic Graph-based Anomaly Detection for multivariate time series analysis in cyber-physical systems