ArticleScientific reports2025
Robust fault detection and classification in power transmission lines via ensemble machine learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Optimized ANN-RF hybrid model with optuna for fault detection and classification in power transmission systems.Scientific reports · 2025Article
- Modifier guided resilient CNN inference enables fault-tolerant edge collaboration for IoT.Scientific reports · 2025Article
- Band level similarity reduction and deep dual signal fusion for reliable diagnosis of power insulator defects.Scientific reports · 2025Article
- A Fault Diagnosis Method for Gas Turbine Rolling Bearings with Variable Speed Based on Dynamic Time-Varying Response and Joint Attention Mechanism.Sensors (Basel, Switzerland) · 2025Article
- Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques.Scientific reports · 2025Article
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Authors and funding
7 authors.
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Abstract
Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms-including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks-are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness. Results indicate that RF-LSTM Tuned KNN achieves a remarkable accuracy of 99.96% on a multi-label dataset, outperforming RF (97.50%) and KNN (96.55%). In binary classification, KNN attains the highest accuracy of 99.85%, closely followed by RF at 99.72%. This methodology provides significant advancements in fault detection capabilities, offering valuable insights for improving grid reliability and stability, and ensuring a more resilient power supply.
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