Dual-attention-enhanced Alexnet for fault diagnosis in multi-energy complementary power systems

Hybrid deep learning-driven explainable AI framework for fault detection and classification in smart power grids.

A hybrid deep learning-based model that combines convolutional neural networks and long short-term memory with explainable artificial intelligence to detect and classify faults accurately and interpretably to intelligent fault management in a contemporary smart grid is suggested.

Transformer fault diagnosis and prediction method based on hybrid neural network

Transformer Fault Diagnosis Method Integrating Long Short-Term Memory Network and Enzyme Action Optimization

Dissolved gas analysis (DGA)-based transformer fault diagnosis is often affected by class imbalance, limited feature representation, and sensitivity to model hyperparameters. To address these issues, this paper proposes a novel computational method that integrates a long short-term memory network with a bio-inspired numerical optimization strategy. K-means SMOTE is first applied to balance the training data. A multi-scale convolutional neural network extracts local fault features under different receptive fields, after which an LSTM models the ordered dependencies among the extracted representations. A multi-head attention mechanism is further introduced to capture global feature interactions and enhance discriminative information. Finally, an enzyme action optimization algorithm is applied as a global numerical optimization approach for adaptive hyperparameter tuning, improving convergence behavior and diagnostic accuracy. Experimental results demonstrate that the proposed framework achieves improved accuracy and stability compared with existing methods, highlighting its potential applicability to a broad class of engineering fault diagnosis and condition monitoring problems.

A Multi-Scale Convolutional Neural Network with Residual Blocks and LSTM for Multi-Step Forecasting of Electricity Load

Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation.

An Attention-Based TabNet Framework for Fault Detection and Diagnosis in HVAC Systems with Limited Labeled Data

Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring energy efficiency, occupant comfort, and sustainability in modern buildings. Their complex operational dynamics,however, make them prone to faults that, if undetected, can result in higher energy consumption, reduced comfort, and costly maintenance. Existing approaches to fault detection and diagnosis (FDD) often face challenges related to scalability and interpretability, with many relying heavily on Transformer-or GNN-based models. This study introduces an enhanced TabNet-based semi-supervised framework for multi-class HVAC fault detection, incorporating rigorous pseudo-label generation via Label Propagation, cost-sensitive learning for imbalanced classes, and hybrid feature engineering. Validated on the 2022 LBNL FDD FCU dataset, the model achieves an accuracy of 82.1%, along with high ROC-AUC and F1-scores values of 96.1% and 82.2% respectively, while its attention-based feature importance provided insights into the most influential operational variables. These results demonstrate that the TabNet framework effectively balances predictive power and interpretability, making it a practical solution for semi-supervised FDD in HVAC systems. The proposed approach contributes to building automation by offering a transparent and reliable pathway for fault detection.

TransFAD: A Hybrid CNN–LSTM and Ensemble Learning Framework for Transmission Line Fault Detection and Classification

In this paper, a hybrid TransFAD system, which abides by voltage and current measurements is introduced and uses these measurements as a basis to detect and classify transmission line faults. Protection of a power system involves fast and reliable fault detection. But in the real world, the noise and transient behavior as well as class imbalance can impair the operation of traditional approaches. Current methods utilize either handcrafted characteristics or individual learning models, allowing them to be inadequate to allow characterization of both sub-instantaneous electrical attributes and temporal fault dynamics. To overcome it, TransFAD integrates classical machine-learning-based classifiers with a CNN-LSTM-based deep learning model. Representation of system imbalance is done using engineered electrical features, and the CNN-LSTM learns spatial and temporal patterns directly on sequence of signal signals. A weighted ensemble strategy is used to combine the predictions of the individual models in order to enhance robustness and accuracy. The efficacy of TransFAD is shown with reference to a publicly accessible electrical faults detection dataset, in which it is assessed by different faults and non-fault conditions and contrasted with standalone models.