Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 171-176· 0 citations· 29 references
Abstract
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.
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
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.
Hafizh Tri Januar, D. Purwanto, D. Kuswidiastuti· International Seminar on Int...· 0 citations
An ensemble learning-based technique has been proposed for fault detection in power transmission lines by using Deep Q-Networks (DQN) in conjunction with standard classifiers such as Naive Bayes, Multilayer Perceptron (MLP), Logistic Regression, and Deep Forest.
Sandeep Godhade, Jayendra Kumar· International Journal of Ele...· 0 citations
Rapid and accurate identification of various faults occurring in transmission lines is essential for restoring normal line operation. However, existing transmission line fault diagnosis methods still face challenges in terms of noise immunity and diagnostic accuracy. To address these issues, this paper proposes a deep learning method based on recurrence plots and a convolutional neural network–bidirectional gated recurrent unit–attention mechanism model. The voltage and current signals of transmission lines are transformed into recurrence plots in both the time and frequency domains. Parallel convolutional neural networks are then employed to extract local features from the two domains, while bidirectional gated recurrent units are used to capture temporal dependencies. Furthermore, multi-head self-attention and cross-attention mechanisms are introduced to enhance key features within each domain and achieve adaptive fusion of inter-domain feature information. A transmission line model is established in Simulink to collect data under various fault conditions and influencing factors, thereby verifying the effectiveness and adaptability of the proposed method. Experimental results show that the proposed method achieves fault recognition accuracies of 99.63%, 96.68%, and 75.38% under NL1, NL2, and NL3 Gaussian-noise conditions, respectively, and maintains accuracies of 99.02%, 95.93%, and 72.43% under mixed-noise conditions. Compared with other deep learning models, the proposed method demonstrates higher diagnostic accuracy and stronger robustness.
Fei Long, Long Hong, Zhenman Gao· Processes· 0 citations
Abstract. The explosive growth of grid-connected renewable energy systems (RES) has increased the complexity of the operation of the modern power infrastructure, making the detection of the faults reliably an inevitable condition of the stable functioning and safety. Traditional single-algorithm and rule-based monitoring systems are not sufficiently flexible or discriminatory to distinguish between the many varieties of faults that occur in photovoltaic (PV) arrays, wind turbines, battery management systems, and grid-tie inverters. The paper suggests 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. Publicly available SCADA and lab bench data were used to create a curated multi-source dataset of 8,400 labelled samples to represent five operational states. The proposed framework achieved an accuracy of 97.8, a macro-averaged F1-score of 97.1, and a Matthews Correlation Coefficient (MCC) of 0.972, outperforming all the compared baseline methods at least by 3.3 percentage points. The findings verify the effectiveness of the hybrid stacking paradigm in identifying faults in real-time and multiple classes in heterogeneous renewable energy settings.
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
Electrical current signals provide essential information about the health and performance of electrical systems. Identifying distorted signals is critical for the early detection of faults in electrical systems, and in turn, helps prevent damage, instability, and loss of efficiency. This paper presents a study on classifying healthy and faulty sine-wave signals using convolutional neural networks. A dataset of 200 images was constructed, to provide diverse waveform variations, and used to train and test a customized Convolutional Neural Networks (CNN) in addition to three pretrained CNNs: SqueezeNet, GoogLeNet, and ResNet-50. Each pretrained network was fine-tuned through transfer learning, and data augmentation was applied to improve generalization. Experimental results show that ResNet-50 achieved the highest validation accuracy of 98.33%, while SqueezeNet and GoogLeNet reached 96.67%. Testing on unseen current signal images confirmed that deeper models were more effective in detecting small waveform distortions. The results demonstrate the suitability of CNN-based approaches for waveform classification and highlight the importance of model depth and dataset variation. This study contributes to the field of predictive maintenance by providing an exploration of a simple, cost-effective, and accurate method for fault detection in single phase induction motors. It opens the door for further research into machine learning applications in fault diagnosis in other types of motors and electrical systems.
M. Shatnawi, Mariam Alsaqqaf, Salihah Almenhali et al.· 2026 6th International Confe...· 0 citations