Anwar Tahir, Mu Chaoxu, Yousaf Muhammad Zain, Khan Wajid, Khalid Saqib, Hourani Ahmad O, Zaitsev Ievgen
School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.
Center for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang University, Zhuji, 311816, Zhejiang, China.
Sci Rep. 2025 Jan 20;15(1):2549. doi: 10.1038/s41598-025-86554-2.
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.
输电线路对于长距离输电至关重要，但由于故障可能会扰乱供电并带来安全风险，它们面临着可靠性挑战。本研究通过分析输电线路各相的电压和电流模式，引入了一种用于故障检测和分类的新方法。利用包含各种故障场景的综合数据集，对包括随机森林（RF）、K近邻（KNN）和长短期记忆（LSTM）网络在内的各种机器学习算法进行了评估。提出了一种集成方法，即RF-LSTM调优KNN，以提高检测精度和鲁棒性。结果表明，RF-LSTM调优KNN在多标签数据集上的准确率达到了99.96%，优于RF（97.50%）和KNN（96.55%）。在二元分类中，KNN的准确率最高，为99.85%，紧随其后的是RF，为99.72%。该方法在故障检测能力方面取得了重大进展，为提高电网可靠性和稳定性提供了有价值的见解，并确保了更具弹性的电力供应。
