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

Mei et al., 2022 - Google Patents

A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations

Mei et al., 2022

View PDF @Full View
Document ID
2576631166742096286
Author
Mei F
Li X
Zheng J
Sha H
Li D
Publication year
Publication venue
IET Generation, Transmission & Distribution

External Links

Snippet

The utilization of data‐driven artificial intelligence technology for ultra‐high voltage converter valve state assessment can improve efficiency and accuracy, but there are still problems such as imbalance of raw data and poor model interpretability. This paper aims to …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • 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
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Senoussaoui et al. Combining and comparing various machine‐learning algorithms to improve dissolved gas analysis interpretation
Sun et al. Research on multi‐attribute decision‐making in condition evaluation for power transformer using fuzzy AHP and modified weighted averaging combination
Tang et al. Hybrid method for power system transient stability prediction based on two‐stage computing resources
Wang et al. Early warning method for transmission line galloping based on SVM and AdaBoost bi‐level classifiers
Zhang et al. Power transformer fault diagnosis considering data imbalance and data set fusion
Wong et al. Computational intelligence for preventive maintenance of power transformers
Hu et al. A novel method for transformer fault diagnosis based on refined deep residual shrinkage network
Mei et al. A data‐driven approach to state assessment of the converter valve based on oversampling and Shapley additive explanations
Wang et al. A novel adversarial transfer learning in deep convolutional neural network for intelligent diagnosis of gas‐insulated switchgear insulation defect: A DATCNN for GIS insulation defect diagnosis
Zhang et al. Online power system dynamic security assessment with incomplete PMU measurements: A robust white‐box model
Zhang et al. Robust classification model for PMU‐based on‐line power system DSA with missing data
Wang et al. Transient stability evaluation model based on SSDAE with imbalanced correction
Wang et al. Transient stability assessment model with improved cost‐sensitive method based on the fault severity
CN113343581B (en) Transformer fault diagnosis method based on graph Markov neural network
Yang et al. Investigating black-box model for wind power forecasting using local interpretable model-agnostic explanations algorithm: Why should a model be trusted?
Su et al. Spatial‐temporal attention and GRU based interpretable condition monitoring of offshore wind turbine gearboxes
Han et al. Research on quality problems management of electric power equipment based on knowledge–data fusion method
Meng et al. Online monitoring technology of power transformer based on vibration analysis
Zu et al. A simple gated recurrent network for detection of power quality disturbances
Chang et al. Improved VMD‐KFCM algorithm for the fault diagnosis of rolling bearing vibration signals
Wang et al. A hybrid 3DSE-CNN-2DLSTM model for compound fault detection of wind turbines
Ma et al. Holistic performance evaluation framework: power distribution network health index
Esmaeili Nezhad et al. A review of the applications of machine learning in the condition monitoring of transformers
Chen Review on supervised and unsupervised learning techniques for electrical power systems: Algorithms and applications
Li et al. A framework for predicting network security situation based on the improved LSTM