A deep learning-based fault detection and classification in smart electrical power transmission system
Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plan...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43647/ http://umpir.ump.edu.my/id/eprint/43647/1/A%20deep%20learning-based%20fault%20detection%20and%20classification%20in%20smart%20electrical.pdf |
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| author | Khaleefah, Shihab Hamad Mostafa, Salama A. Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Siti Salwani, Yaacob Alanda, Alde |
| author_facet | Khaleefah, Shihab Hamad Mostafa, Salama A. Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Siti Salwani, Yaacob Alanda, Alde |
| author_sort | Khaleefah, Shihab Hamad |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models. |
| first_indexed | 2025-11-15T03:52:35Z |
| format | Article |
| id | ump-43647 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:52:35Z |
| publishDate | 2024 |
| publisher | Politeknik Negeri Padang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-436472025-01-21T08:43:22Z http://umpir.ump.edu.my/id/eprint/43647/ A deep learning-based fault detection and classification in smart electrical power transmission system Khaleefah, Shihab Hamad Mostafa, Salama A. Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Siti Salwani, Yaacob Alanda, Alde QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models. Politeknik Negeri Padang 2024 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/43647/1/A%20deep%20learning-based%20fault%20detection%20and%20classification%20in%20smart%20electrical.pdf Khaleefah, Shihab Hamad and Mostafa, Salama A. and Gunasekaran, Saraswathy Shamini and Khattak, Umar Farooq and Siti Salwani, Yaacob and Alanda, Alde (2024) A deep learning-based fault detection and classification in smart electrical power transmission system. International Journal on Informatics Visualization, 8 (2). pp. 812-818. ISSN 2549-9904. (Published) https://doi.org/10.62527/joiv.8.2.2701 https://doi.org/10.62527/joiv.8.2.2701 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Khaleefah, Shihab Hamad Mostafa, Salama A. Gunasekaran, Saraswathy Shamini Khattak, Umar Farooq Siti Salwani, Yaacob Alanda, Alde A deep learning-based fault detection and classification in smart electrical power transmission system |
| title | A deep learning-based fault detection and classification in smart electrical power transmission system |
| title_full | A deep learning-based fault detection and classification in smart electrical power transmission system |
| title_fullStr | A deep learning-based fault detection and classification in smart electrical power transmission system |
| title_full_unstemmed | A deep learning-based fault detection and classification in smart electrical power transmission system |
| title_short | A deep learning-based fault detection and classification in smart electrical power transmission system |
| title_sort | deep learning-based fault detection and classification in smart electrical power transmission system |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/43647/ http://umpir.ump.edu.my/id/eprint/43647/ http://umpir.ump.edu.my/id/eprint/43647/ http://umpir.ump.edu.my/id/eprint/43647/1/A%20deep%20learning-based%20fault%20detection%20and%20classification%20in%20smart%20electrical.pdf |