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).
The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes, whereas conventional voltage and current measurements alone represent a simpler and more stable alternative, whereas phase-angle measurements may be inc...
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
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.
Qing-Hua 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 pr...
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...
Ukut, Uwem, Isong, Nsebong Opura, A. Obot et al.· Journal of Energy Research a...· 0 citations
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, validating the effectiveness of the proposed hybridisation strategy for intelligent transmission system protection.
Kudu Abubakar Mohammed, Balogun Monsurat O., Adesina M. Lambe et al.· Communication in Physical Sc...· 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...
A. Gopalakrushna· Materials Research Proceedin...· 0 citations
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