Prediction of bundle-conductor ampacity based on transformer-LSTM
The traditional method cannot meet the demand of new power system for dynamic regulation of transmission lines. In order to solve this defect, based on finite element simulation and neural network, an overhead bundle-conductor dynamic bundle-conductor ampacity prediction method is proposed in this p...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1603239/full |
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| author | Song Bao Hua Bao Miao Jin Yong Ruan Yunfei Shi Chao Yang |
| author_facet | Song Bao Hua Bao Miao Jin Yong Ruan Yunfei Shi Chao Yang |
| author_sort | Song Bao |
| collection | DOAJ |
| description | The traditional method cannot meet the demand of new power system for dynamic regulation of transmission lines. In order to solve this defect, based on finite element simulation and neural network, an overhead bundle-conductor dynamic bundle-conductor ampacity prediction method is proposed in this paper. Considering the four bundle- JL/G1A-400/35 steel-core aluminum stranded wire, the three-dimensional electric-thermal-fluid coupling model of the conductor is established by using the synergistic optimization of transformer and long-short-term memory neural network (LSTM). The results show that the mean square error and average absolute error of the proposed model are 31.14 and 6.93, respectively. Compared with the bidirectional long and short-term memory network (BiLSTM), the mean square error and average absolute error are reduced by 74.55% and 7.35%, respectively. The maximum improvement of load capacity prediction margin is 10.04%. It can effectively tap the dynamic potential of transmission lines, and provide technical support for real-time scheduling of smart grid. |
| format | Article |
| id | doaj-art-3510bc4ee96c4f82904cb8e4b079cd83 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-3510bc4ee96c4f82904cb8e4b079cd832025-08-20T03:28:41ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-07-011310.3389/fphy.2025.16032391603239Prediction of bundle-conductor ampacity based on transformer-LSTMSong BaoHua BaoMiao JinYong RuanYunfei ShiChao YangThe traditional method cannot meet the demand of new power system for dynamic regulation of transmission lines. In order to solve this defect, based on finite element simulation and neural network, an overhead bundle-conductor dynamic bundle-conductor ampacity prediction method is proposed in this paper. Considering the four bundle- JL/G1A-400/35 steel-core aluminum stranded wire, the three-dimensional electric-thermal-fluid coupling model of the conductor is established by using the synergistic optimization of transformer and long-short-term memory neural network (LSTM). The results show that the mean square error and average absolute error of the proposed model are 31.14 and 6.93, respectively. Compared with the bidirectional long and short-term memory network (BiLSTM), the mean square error and average absolute error are reduced by 74.55% and 7.35%, respectively. The maximum improvement of load capacity prediction margin is 10.04%. It can effectively tap the dynamic potential of transmission lines, and provide technical support for real-time scheduling of smart grid.https://www.frontiersin.org/articles/10.3389/fphy.2025.1603239/fulloverhead line ampacitydynamic regulationbundle-conductorlongshort-term memorytransformer |
| spellingShingle | Song Bao Hua Bao Miao Jin Yong Ruan Yunfei Shi Chao Yang Prediction of bundle-conductor ampacity based on transformer-LSTM Frontiers in Physics overhead line ampacity dynamic regulation bundle-conductor longshort-term memory transformer |
| title | Prediction of bundle-conductor ampacity based on transformer-LSTM |
| title_full | Prediction of bundle-conductor ampacity based on transformer-LSTM |
| title_fullStr | Prediction of bundle-conductor ampacity based on transformer-LSTM |
| title_full_unstemmed | Prediction of bundle-conductor ampacity based on transformer-LSTM |
| title_short | Prediction of bundle-conductor ampacity based on transformer-LSTM |
| title_sort | prediction of bundle conductor ampacity based on transformer lstm |
| topic | overhead line ampacity dynamic regulation bundle-conductor longshort-term memory transformer |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1603239/full |
| work_keys_str_mv | AT songbao predictionofbundleconductorampacitybasedontransformerlstm AT huabao predictionofbundleconductorampacitybasedontransformerlstm AT miaojin predictionofbundleconductorampacitybasedontransformerlstm AT yongruan predictionofbundleconductorampacitybasedontransformerlstm AT yunfeishi predictionofbundleconductorampacitybasedontransformerlstm AT chaoyang predictionofbundleconductorampacitybasedontransformerlstm |