Aug 2026· International Journal of Electronics and Communication Engineering· 0 citations· 32 references
TL;DR
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.
Abstract
The continuous complexities and the requirement of reliable power transmission have led to the necessity for advanced fault detection systems in order to ensure stability and safety in electrical grids. In this study, 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. The proposed algorithm uses reinforcement learning for optimizing classifier weights. Voltage and current waveforms are used as input data for extracting features and performing classification. The performance of the proposed technique is highly improved, providing 99.10% accuracy, 99.40% precision, 99.70% sensitivity, and 99.50% F1-score, which is far better than the existing techniques. Moreover, the proposed framework provides reduced processing time (12-13 milliseconds) and low memory consumption (150-152 megabytes).
This study proposes an artificial neural network-based approach for the detection and classification of faults occurring in high voltage alternating current (HVAC) power transmission lines. The study considers 12 classes comprising 11 fault types and one healthy state. Unlike traditional approaches that rely on extensive feature-extraction procedures, this study directly employs measured three-phase voltage, current, and phase-angle quantities as ANN inputs, thereby avoiding computationally intensive signal decomposition and handcrafted feature extraction stages. The model was evaluated using regression-oriented metrics, including mean squared error (MSE) and correlation coefficient (R). Furthermore, 5-fold cross-validation showed that the proposed ANN achieved better regression performance than GPR, SVR, and Kernel Regression models. Additional robustness analyses performed under different loading conditions and fault resistance values further demonstrated the generalization capability of the proposed ANN models under varying operating conditions. To evaluate the practical contribution of phase-angle information, a classification-based ablation study compared a 6-input ANN using only three-phase voltage and current measurements with a 12-input ANN including phase-angle measurements. Under identical test conditions, the 6-input and 12-input classifiers achieved accuracies of 88.51% and 84.73%, respectively, with macro F1-scores of 0.8789 and 0.8374. Repeated-training analysis further showed that the six-input configuration achieved higher mean performance and lower variability. The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes. Conventional voltage and current measurements alone therefore represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
Power grid engineering is necessary for the distribution of power, but problem detection and categorization are made extremely difficult by their growing complexity, particularly with the incorporation of renewable energy sources. Decision trees and support vector machines are two examples of fault detection techniques that frequently fail to handle the dynamic and non-stationary character of power grid data. These techniques’ efficacy in real-time defect identification is limited because they are unable to capture the complex relationships and temporal dependencies present in time-series data. Furthermore, a lot of conventional models are unable to generalize to different kinds of problems, which results in errors and delays in fault identification. For improved fault detection and classification in power grids, this research suggests a hybrid approach that combines Temporal Fusion Transformers (TFT) with Long Short-Term Memory (LSTM) Neural networks. While the LSTM neural network is used to represent sequential data, the TFT model is particularly good at capturing complicated linkages in time-series data. By combining these two models, the suggested approach can improve grid efficiency and dependability by offering precise fault forecasts in real-time. The findings show that the suggested hybrid model works better than conventional techniques, with a fault classification accuracy of 98.66% as opposed to decision trees’ 97.42% and SE-CDAE’s 97.98% accuracy. The performance of the model demonstrates its potential for real-time implementation in contemporary power grids, guaranteeing improved fault identification and prompt reactions to avert system breakdowns.
Qinghua Chen, Tao Xu, Cheng Zhou et al.· Distributed Generation &...· 0 citations
The diagnosis of faults in power transformers (PT) plays a crucial role in ensuring the reliability and stability of power systems. However, traditional fault detection techniques are prone to low detection accuracy and are not stable enough under complex operating conditions. To address these challenges, this paper proposes a novel hybrid Variational Mode Decomposition (VMD), Graph Attention Network (GAN) with Adaptive Extreme Learning Machine (AELM) method for a transformer fault diagnosis framework. VMD can efficiently extract discriminative frequency bands of the transformer signals, GAN can dynamically learn the importance and relationship of the extracted feature, and AELM can classify rapidly and accurately with low complexity. The dataset was obtained from simulations of various fault conditions in the Matlab/Simulink. The experimental results demonstrate that the proposed method has an accuracy of 99.5%, a precision of 99.66%, a recall of 99.33%, and an F1-score of 99.5% compared to existing methods. The proposed hybrid framework enables efficient classification capability, feature learning with attention, and adaptive feature extraction, which contributes to improved performance.
Reliable transmission networks underpin national economic development, yet developing countries continue to experience disproportionately high rates of transmission line faults, prolonged outage durations, and constrained investment in protection infrastructure. This review synthesises the current state of knowledge on fault detection, classification, and location techniques applicable to high-voltage transmission systems, with particular attention to the technical, economic, and institutional constraints that shape technology adoption in low- and middle-income power sectors. Conventional protection philosophies based on impedance relaying and overcurrent schemes are examined alongside signal-processing approaches such as wavelet transforms and travelling-wave methods, and against the growing body of work applying machine learning and deep learning architectures, including convolutional neural networks, long short-term memory networks, and hybrid ensembles, to fault diagnosis tasks. The review finds that although artificial-intelligence-based methods report consistently high accuracy under simulated conditions, their transferability to developing-country networks is constrained by sparse instrumentation, weak communication infrastructure, limited synchrophasor coverage, and a shortage of locally labelled fault data. High-impedance faults, series compensation, renewable-integrated feeders, and ageing conductor assets introduce further complications that are underrepresented in the literature, which remains dominated by simulation studies from well-instrumented grids. The review identifies practical pathways for closing this gap, including low-cost phasor measurement architectures, transfer learning from synthetic to field data, and hybrid schemes that combine physics-based fault location with data-driven classification. The synthesis is intended to orient researchers, utility engineers, and regulators in developing economies towards protection strategies that are both technically sound and economically deployable within prevailing infrastructure constraints.
Ukut, Uwem, Isong, Nsebong Opura, A. Obot et al.· Journal of Energy Research a...· 0 citations
The detection of fault and its resolution are crucial in a power transmission line for ensuring unhampered and efficient power supply. These lines are often exposed to unpredictable environmental conditions and therefore encounter several challenges. Most failures in the power system are attributed to these, thereby necessitating the need for quick fault detection and resolution procedures. Research on hybridizing ANN and SVM is limited to fault detection and classification. Work on hybridizing ANN and SVM on IEE 39-bus for three simultaneous diagnostic tasks of fault type classification (LG, LL, LLG, LLL), faulted-line identification, and protection zoning defined as near-end versus far-end fault discrimination in a model is rare. This study bridges this gap by presenting a hybrid ANN-SVM model onfault type classification, fault line identification and protection zone identification in power transmission lines. The proposed framework employs ANN as a nonlinear feature embedding and a Radial Basis Function SVM subsequently classifies using an Error-Correcting Output Codes (ECOC) strategy. Evaluated on fault scenarios from the IEEE 39-bus New England test system simulated in MATLAB/Simulink R2025b, the hybrid model achieves fault type classification accuracy of 97.2%, protection zoning accuracy of 95.8%, and faulted-line identification accuracy of 97.7%, with ROC Area Under the Curve (AUC) values exceeding 0.90. These results consistently outperform standalone nd SVM baselines by 4% to 6% in fault type, 2.9% to 15.9% in protection zoning and 5% to 10% in fault line classification, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, M. Balogun, Adesina M. Lambe et al.· Communication in Physical Sc...· 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