Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 1006-1011· 0 citations· 14 references
Abstract
The availability and reliability of the power transmission system are crucial to ensuring the continuity of the electrical energy supply. Disturbances caused by natural events, vegetation interference, or animal activity can lead to widespread blackouts, necessitating rapid and accurate fault analysis. This study proposes a multi-representation deep learning framework for fault classification using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture driven by raw Disturbance Fault Recorder (DFR) data. To ensure rigorous evaluation and strictly prevent data leakage, an initial dataset of 457 raw DFR recordings is partitioned using a stratified split method. Exactly 87 validation and 54 testing samples are strictly isolated to preserve their real-world integrity, while the remaining 316 training samples are synthetically augmented to a perfectly balanced 1,500 samples (500 per class). These are processed seamlessly into 1D time-series signals and 2D stacked spatial images at a 224x224 resolution. Furthermore, an Uncertainty Rejection strategy utilizing a 60% confidence threshold is implemented to dynamically intercept ambiguous transient anomalies and prevent forced misclassifications. Experimental results demonstrate that the proposed hybrid CNN-LSTM model achieves an overall classification accuracy of 96.3%. While this 3.7% accuracy improvement corresponds to exactly a 2-sample difference on the constrained testing set compared to the established standalone baselines (LSTM and CNN, both at 92.6%), it serves as preliminary evidence demonstrating that the hybrid architecture can resolve specific, ambiguous edge-cases that single-view models fail to classify. The proposed framework offers a reliable, visually-explainable foundation that can be implemented in a monitoring system to assist operators in making faster and more accurate decisions in fault handling.
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.
Tejinder Kaur, Abirami.R, Anitha Rani Palakayala et al.· 2026 International Conferenc...· 0 citations
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.
Udit Mamodiya, Divyanshu Sinha, I. Kishor et al.· Scientific Reports· 0 citations
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
Edris Shamsulhaq, Fikri Arif Wicaksana· Jambura Journal of Electrica...· 0 citations
A new stacking-ensemble hybrid machine learning model that will combine a one-dimensional convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) module, a Random Forest classifier, and an XGBoost gradient booster as base learners under the guidance of a logistic regression meta-learner is suggested.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
Energy retention from losses is the primary goal of fault detection methodology for photovoltaic (PV) solar systems. A fault detection model should be designed effectively to minimize power and cost waste. We propose a novel fault detection and localization method that leverages deep learning techniques for PV systems. The model is a hybrid semantic segmentation method that combines Convolutional Neural Networks (CNN) and multi-level transformer neural networks. We examine our model on four different segmentation datasets, which have varied characteristics and different capturing conditions, to ensure our model generalization. Using aerial inspection, the first thermal dataset images give a high performance in locating the faulty cells in PV arrays with an accuracy and global precision of 99.89%, a mean average precision (mAP) of 88.73%, a mean intersection over union (mIoU) of 76.29%, and 84.48% of mean dice (mDice, or mean/unweighted F1 score). We use three electroluminescence datasets to investigate the precise location and class of different fault types and minor cracks at the cell level. The second dataset result achieved perfect detection and segmentation of anomaly areas in cells with 99% accuracy, 96.36% mIoU, 98.13% mDice, and 98.41% mAP. The first two datasets contain binary segmentation images with fault and no-fault classes; to accommodate multiple classes, we have utilized the third and fourth datasets. The third dataset, comprising five PV failure classes, is evaluated against other models, yielding a superior performance of 96.27% precision, 77.64% mAP, 57.3% mIoU, and 68.45% mDice. The final dataset has 29 segmentation classes for testing 25 classes, for which it achieves a precision of 95.05%, 66.7% mAP, 49.3% mIoU, and 59.3% mDice.
E. A. Ramadan, Nada M. Moawad, B. Abou-Zalam et al.· Scientific Reports· 0 citations
A new hybrid deep learning architecture, combining one-dimensional convolutional neural networks (1D-CNN) with bidirectional long short-term memory (BiLSTM) networks to the problem of automatic detection and early forecasting of mechanical faults based on raw vibration signals is suggested.
N. Bharani· Materials Research Proceedin...· 0 citations
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