{"id":9694,"date":"2026-08-05T21:58:36","date_gmt":"2026-08-05T13:58:36","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9694"},"modified":"2026-08-06T23:19:07","modified_gmt":"2026-08-06T15:19:07","slug":"jase-202611-34-024","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-024","title":{"rendered":"Paper Real-time Data Processing of Wide-area Digital Metering Equipment for Electric Power Based on Deep Learning Algorithms"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

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Dongsheng Xue, Jiaxing Zhao, Zhengying Yang<\/i><\/a>, Wenwen Wang, Na Wang, and Yingcai Gao<\/p>\n\n\n\n

State Grid Shanxi Electric Power Co., Ltd., Yangquan Electric Power Supply Company, Yangquan 045000, Shanxi, China<\/p>\n<\/div>\n\n\n\n

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Received: April 04, 2026
Accepted: May 13, 2026
Publication Date: August 05, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Visualizing Diagnostic Feature Separability<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.024<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The increasing complexity of modern power grids and the integration of advanced monitoring technologies have significantly enhanced real-time system monitoring. Phasor Measurement Units (PMUs) play a vital role by providing accurate measurements of voltage, current, frequency, and rate of change of frequency (ROCOF). However, conventional fault detection methods often struggle to identify diverse transmission line faults due to dynamic grid disturbances and large volumes of time-series data. To address this challenge, this study proposes a deep learning-based framework for accurate fault detection and classification using a PMU dataset derived from the IEEE 39-bus power system. The system considers various fault types, including line-to-ground, line-to-line, double line-to-ground, and three-phase faults. The methodology includes data preprocessing steps such as cleaning, noise filtering, segmentation, and normalization, followed by statistical time-window-based feature extraction. An attention-based Bidirectional Long Short-Term Memory (BiLSTM) model is employed to capture temporal dependencies and identify critical fault patterns. Experimental results demonstrate high performance, achieving accuracy, precision, recall, and F1-score of 98.7%, 97.9%, 98.3%, and 98.1%, respectively.<\/p>\n\n\n\n

Keywords: Phasor Measurement Units (PMU); Fault Detection; Deep Learning; BiLSTM; Power System Monitoring<\/em><\/p>\n\n\n\n

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