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.
Abstract
The stable detection of faults in smart power grids is essential when focusing on the stable operation and the reduction of the downtime. In this paper, the author suggests a hybrid deep learning-based model that combines convolutional neural networks (CNN) and long short-term memory (LSTM) with explainable artificial intelligence (XAI) to detect and classify faults accurately and interpretably. The model aims at capturing the spatial and time-varying attributes of multivariate electrical signatures such as voltage, current, and frequency changes. A combination of real time sensor measurements and simulated fault conditions are used which includes several fault classes including LG, LL, LLG, and three phase faults. Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 97.84%, with an F1-score of 97.08%, outperforming conventional CNN and LSTM models by more than 3%. The framework is also able to work in a noisy environment with a stable performance of less than 2% performance decrease, and inference latency of 18 ms, which can be deployed in real-time. Besides, the combination of SHAP and attention processes improves the interpretability through the detection of the crucial features that lead to fault prediction. The findings suggest that the suggested solution is a powerful, scalable, and clear solution when it comes to intelligent fault management in a contemporary smart grid.
Results indicate that artificial intelligence can significantly strengthen the resilience and automation of next-generation smart grid infrastructures.
T. Anvesh, Akshaya Chelpuri, Ambati Chandu· International Scientific Jou...· 0 citations
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
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
As a result of the rapid expansion in the integration of renewable energy and the coupling of various aspects like generation, grid, load, and storage, the most adopted methods in power fault diagnosis today have been the use of convolutional neural networks. Despite these advances, the existing models suffer from problems related to poor feature selection and inadequate representation of cross-time-scale interactions in multi-source and heterogeneous input environments. This challenge is further compounded by the distinct operational cycles and characteristics of multi-energy complementary units. This study builds a dual-attention optimized AlexNet fault diagnosis model. It introduces channel attention, spatial attention, and hybrid dilated convolution into the AlexNet backbone. The results show that the final loss decreases to 0.20, and the diagnostic accuracy under standard testing conditions reaches 97.88%. In a 30 min short-term monitoring scenario, the macro-average F1 score increases from 91.82% to 97.18%, and the macro-average recall increases from 91.06% to 96.79%. According to the findings, the dual-attention network together with the hybrid dilation approach improves the performance of multi-energy complementary power system fault diagnosis by increasing the convergence speed, monitoring accuracy, and reducing the number of false alarms. The proposed model is an efficient tool in power system monitoring and fault detection.
Xiuli Zheng, Yingtang Zhang, Jiyang Zhang et al.· Engineering Research Express· 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
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