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References from Multi-resolution perceiver-based residual attention for explainable fault diagnosis in power transmission systems. Local targets link to admitted publications; unresolved targets remain external evidence.
Deep learning through lstm classification and regression for transmission line fault detection, diagnosis and location in large-scale multi-machine power systems
10.1016/j.measurement.2021.109330 · 2021 · External reference
Spatio-temporal attention-based deep learning for smart grid demand prediction
10.3390/electronics14132514 · 2025 · External reference
Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction
10.1109/tie.2017.2721922 · 2017 · External reference
Unresolved reference
2023 · External reference
“Tlfed: federated learning-based 1d-cnn-lstm transmission line fault location and classification in smart grids,”
10.1109/icaiic60209.2024.10463418 · 2024 · External reference
Explainable artificial intelligence for time series using attention mechanism: Application to wind turbine fault detection
10.1109/access.2025.3621003 · 2025 · External reference
Fast and accurate fault detection and classification in transmission lines using extreme learning machine
10.1016/j.prime.2023.100107 · 2023 · External reference
Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification
10.3389/fphy.2025.1616367 · 2025 · External reference
Perceiver: General perception with iterative attention
2021 · External reference
“Fault detection and diagnosis in Tennessee Eastman process with deep autoencoder,”
2023 · External reference
“Explainable fault detection in electrical power systems,”
10.1109/commnet68224.2025.11288838 · 2025 · External reference
Perceiver-based cdf modeling for time series forecasting
2023 · External reference
“Leveraging perceiver io for efficient compression of temporal signals a memory constrained approach,”
10.1145/3726122.3726165 · 2024 · External reference
Sustainable and reliable operation of ev charging infrastructure: a lightweight prototype-driven contrastive learning framework for fault diagnosis under class-imbalanced conditions
10.3390/su18115783 · 2026 · External reference
A study on interpretable electric load forecasting model with spatiotemporal feature fusion based on attention mechanism
10.3390/technologies13060219 · 2025 · External reference
“USD: unsupervised soft contrastive learning for fault detection in multivariate time series,”
10.1109/icassp49660.2025.10890487 · 2025 · External reference
A spatial-temporal variational graph attention autoencoder using interactive information for fault detection in complex industrial processes
10.1109/tnnls.2023.3328399 · 2023 · External reference
Fault detection in power transformers using frequency response analysis and machine learning models
10.3390/app15052406 · 2025 · External reference
Graph neural networks for the localization of faults in a partially observed regional transmission system
10.1111/sjos.12763 · 2025 · External reference
Contrastive learning for fault detection and diagnostics in the context of changing operating conditions and novel fault types
10.3390/s21103550 · 2021 · External reference
Machine learning advances in transmission line fault detection: a literature review
10.30574/ijsra.2024.12.1.1150 · 2024 · External reference
Spatial-temporal bearing fault detection using graph attention networks and lstm
2024 · External reference
A novel fault diagnosis and accurate localization method for a power system based on graphsage algorithm
10.3390/electronics14061219 · 2025 · External reference
“Spatio-temporal parameter identification in power grids using graph neural networks,”
10.1109/powercon66300.2025.11294745 · 2025 · External reference
Industrial fault detection using contrastive representation learning on time-series data
10.1016/j.ifacol.2023.10.1456 · 2023 · External reference
