Fault line selection of power distribution system via improved bee colony algorithm based deep neural network
A fault line selection approach on the basis of modified artificial bee colony optimization deep neural network (ACB-DNN) is presented to address the difficulties in choosing a fault line in electric current grounding systems for small electric currents. Matlab/Simulink is utilized to acquire the ze...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722020054 |
| _version_ | 1797952810703126528 |
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| author | Na Wang |
| author_facet | Na Wang |
| author_sort | Na Wang |
| collection | DOAJ |
| description | A fault line selection approach on the basis of modified artificial bee colony optimization deep neural network (ACB-DNN) is presented to address the difficulties in choosing a fault line in electric current grounding systems for small electric currents. Matlab/Simulink is utilized to acquire the zero-sequence electric current in the faulty circuit, the training sample dataset of the modified deep neural network (DNN) is used to output the line selection result after training. The training time is reduced to a certain extent by enhancing the bee colony method to optimize the network’s weights. The simulation results show that this algorithm decreases training time, enhances judgment accuracy, and is robust to system topology, all of which fulfill the demands of real application. |
| first_indexed | 2024-04-10T22:53:03Z |
| format | Article |
| id | doaj.art-d6ed384fce3940698f1262fce8d1405c |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:53:03Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-d6ed384fce3940698f1262fce8d1405c2023-01-15T04:22:03ZengElsevierEnergy Reports2352-48472022-11-0184353Fault line selection of power distribution system via improved bee colony algorithm based deep neural networkNa Wang0Shenyang Institute of Engineering, Shenyang 110136, ChinaA fault line selection approach on the basis of modified artificial bee colony optimization deep neural network (ACB-DNN) is presented to address the difficulties in choosing a fault line in electric current grounding systems for small electric currents. Matlab/Simulink is utilized to acquire the zero-sequence electric current in the faulty circuit, the training sample dataset of the modified deep neural network (DNN) is used to output the line selection result after training. The training time is reduced to a certain extent by enhancing the bee colony method to optimize the network’s weights. The simulation results show that this algorithm decreases training time, enhances judgment accuracy, and is robust to system topology, all of which fulfill the demands of real application.http://www.sciencedirect.com/science/article/pii/S2352484722020054Power distribution systemPower fault detectionDeep neural networkArtificial intelligence |
| spellingShingle | Na Wang Fault line selection of power distribution system via improved bee colony algorithm based deep neural network Energy Reports Power distribution system Power fault detection Deep neural network Artificial intelligence |
| title | Fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| title_full | Fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| title_fullStr | Fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| title_full_unstemmed | Fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| title_short | Fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| title_sort | fault line selection of power distribution system via improved bee colony algorithm based deep neural network |
| topic | Power distribution system Power fault detection Deep neural network Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722020054 |
| work_keys_str_mv | AT nawang faultlineselectionofpowerdistributionsystemviaimprovedbeecolonyalgorithmbaseddeepneuralnetwork |