Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique
Abstract Power systems are becoming increasingly complex due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability and stability depend on limiting the fault currents. Analysis of faults is essential for the selection of appropriate relays...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Artificial Intelligence |
| Online Access: | https://doi.org/10.1007/s44163-025-00282-0 |
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| _version_ | 1849688336112812032 |
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| author | Arif Ullah Muhammad Zain Yousaf Abdul Aziz Abdul Jabbar Wajid Khan Baseem Khan |
| author_facet | Arif Ullah Muhammad Zain Yousaf Abdul Aziz Abdul Jabbar Wajid Khan Baseem Khan |
| author_sort | Arif Ullah |
| collection | DOAJ |
| description | Abstract Power systems are becoming increasingly complex due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability and stability depend on limiting the fault currents. Analysis of faults is essential for the selection of appropriate relays and other safety systems. In this research work fault analysis, fault detection, and data prediction of long transmission lines are discussed. Different parameters are analyzed at each point of distance using MATLAB/Simulink for long transmission lines. Through these results, it is concluded that with the increase in distance, the parameters change, so the detection response of the Mho relay also varies. Moreover, in this research work, the Mho relay response at different points is tested through Simulink software for long transmission lines. As it is concluded that manually it is very difficult to test the response of Mho relay at each point, the artificial intelligence technique of long short-term memory (LSTM) is used for the prediction of the data for the next long transmission line. Lastly, in this work, a long short-term memory network is used to predict the parameters. The LSTM is then used to find the predicted data of subsequent transmission line from previous data. The artificial intelligence technique successfully utilized for the detection of long transmission line fault. |
| format | Article |
| id | doaj-art-d6fd246baae0493aace6ad1dde761bf6 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-d6fd246baae0493aace6ad1dde761bf62025-08-20T03:22:03ZengSpringerDiscover Artificial Intelligence2731-08092025-05-015111710.1007/s44163-025-00282-0Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM techniqueArif Ullah0Muhammad Zain Yousaf1Abdul Aziz2Abdul Jabbar3Wajid Khan4Baseem Khan5School of Electrical Engineering, CECOS University of IT and Emerging SciencesDepartment of Electrical and Electronic Engineering Technology, University of JohannesburgSchool of Electrical Engineering, CECOS University of IT and Emerging SciencesSchool of Electrical Engineering, University of Engineering and TechnologySchool of Electrical and Information Engineering, Tianjin UniversityDepartment of Electrical and Computer Engineering, Hawassa UniversityAbstract Power systems are becoming increasingly complex due to the integration of several power electronic devices. Protection of such systems and augmentation of reliability and stability depend on limiting the fault currents. Analysis of faults is essential for the selection of appropriate relays and other safety systems. In this research work fault analysis, fault detection, and data prediction of long transmission lines are discussed. Different parameters are analyzed at each point of distance using MATLAB/Simulink for long transmission lines. Through these results, it is concluded that with the increase in distance, the parameters change, so the detection response of the Mho relay also varies. Moreover, in this research work, the Mho relay response at different points is tested through Simulink software for long transmission lines. As it is concluded that manually it is very difficult to test the response of Mho relay at each point, the artificial intelligence technique of long short-term memory (LSTM) is used for the prediction of the data for the next long transmission line. Lastly, in this work, a long short-term memory network is used to predict the parameters. The LSTM is then used to find the predicted data of subsequent transmission line from previous data. The artificial intelligence technique successfully utilized for the detection of long transmission line fault. https://doi.org/10.1007/s44163-025-00282-0 |
| spellingShingle | Arif Ullah Muhammad Zain Yousaf Abdul Aziz Abdul Jabbar Wajid Khan Baseem Khan Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique Discover Artificial Intelligence |
| title | Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique |
| title_full | Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique |
| title_fullStr | Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique |
| title_full_unstemmed | Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique |
| title_short | Fault analysis and detection on multiple points in transmission line through Mho relay and its data prediction through LSTM technique |
| title_sort | fault analysis and detection on multiple points in transmission line through mho relay and its data prediction through lstm technique |
| url | https://doi.org/10.1007/s44163-025-00282-0 |
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