Jul 2026· Journal of Energy Research and Reviews· Vol 18, pp. 37-51· 0 citations
Abstract
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.
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
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 incorporated when synchronized phasor information is already available.
Zeynep Bala Duranay, İsmail Anıl Avcı, Mohammed Bushra Mohammed et al.· Symmetry· 0 citations
In order to improve the real-time fault detection capability of power transmission and distribution stations, this paper presents a new approach for fault identification, which combines smart sensing, deep temporal networks, and edge inference. Addressing challenges such as inconsistent sampling frequency, temporal drift, and the difficulty of separating anomalous coupling characteristics from multi-source monitoring signals (including current, voltage, temperature rise, vibration, and PD). Based on this, we propose a deep temporal recognition network consisting of temporal convolutions, residual propagation, and attention aggregation to model the evolution of short-term disturbances, persistent fluctuations, and abrupt anomalies. At the same time, a lightweight reasoning chain is used to build a dynamic early-warning score based on the combination of fault classification probability, state duration, and feature deviation intensity. The results show that the proposed approach achieves 97.3% accuracy, 96.9% F1-score, and 0.984 AUC. In the edge inference response test, the inference latency remains within the range of 22.3–31.4 ms, and the fault response completion rate fluctuates between 92.6% and 94.3%. Compared with the centralized upload mode, whose latency increases from 48.6 ms to a peak of 72.5 ms during high-frequency abnormal updates, the proposed edge inference mechanism maintains lower response delay and more stable real-time fault response capability.
Liu Yang, Jiang-Tao Guo, Meihui Hu et al.· European Conference on Elect...· 0 citations
Power transmission lines are essential components of power systems, and their operating conditions directly affect the safety, stability, and reliability of power supply. In practical operation, transmission lines are susceptible to various fault conditions, such as short circuits, grounding faults, conductor breakage, and insulator abnormalities, due to lightning strikes, strong winds, pollution, equipment aging, and external disturbances. Conventional fault identification methods mainly depend on relay protection signals, manual inspection, and model-based analysis. Although these methods have been widely applied, they often suffer from limited adaptability, insufficient feature representation capability, and reduced identification accuracy under complex operating conditions. To improve the accuracy and intelligence level of transmission line fault diagnosis, this paper proposes a deep learning-based intelligent fault identification method for power transmission lines. The proposed method employs a deep neural network to learn fault characteristics directly from transmission line monitoring data, thereby reducing dependence on manual feature extraction. Through hierarchical feature representation and nonlinear mapping, the method can effectively distinguish different fault patterns and improve fault classification performance. In addition, preprocessing and optimization strategies are introduced to suppress noise interference and enhance model robustness under imbalanced and complex data conditions. Experimental results show that the proposed method achieves better identification accuracy, stability, and generalization performance than conventional methods. The proposed method can provide effective technical support for online fault monitoring, rapid fault diagnosis, and intelligent operation and maintenance of transmission lines.
Zhi-Wei Ni, Wen Chen, Pan Zhou et al.· European Conference on Elect...· 0 citations
The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model.
Nazmun Nahar Karima, M. Hazari, Shameem Ahmad et al.· Energies· 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.
Qinghua Chen, Tao Xu, Cheng Zhou et al.· Distributed Generation &...· 0 citations
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