Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters
Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/4/945 |
| _version_ | 1797298371336077312 |
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| author | Feng Wang Hongbo Lin Ziming Ma |
| author_facet | Feng Wang Hongbo Lin Ziming Ma |
| author_sort | Feng Wang |
| collection | DOAJ |
| description | Aiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional neural network and bidirectional gated recurrent unit were used to construct a transmission line icing thickness prediction model The model incorporated a weighted fusion of soft−dynamic time warping (Soft−DTW) and the icing change rule as the loss function. Optimal weights were determined through the utilization of the grid search algorithm and cross−validation, contributing to an enhancement of the model’s generalization capabilities and a reduction in prediction errors. The results indicate that the proposed prediction model can consider the impact of line operating parameters, avoiding the shortcomings of prediction results conflicting with actual physical laws. Compared with traditional non−mechanical models, the proposed model showed reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 0.26–0.51%, 0.24–0.44%, and 5.77–13.33%, respectively, while the coefficient of determination (R2) increased by 0.07–0.13. |
| first_indexed | 2024-03-07T22:33:54Z |
| format | Article |
| id | doaj.art-b7e6e55cd48744cd91d858f39f89015b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-07T22:33:54Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b7e6e55cd48744cd91d858f39f89015b2024-02-23T15:15:30ZengMDPI AGEnergies1996-10732024-02-0117494510.3390/en17040945Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating ParametersFeng Wang0Hongbo Lin1Ziming Ma2College of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaAiming to improve on the low accuracy of current transmission line icing prediction models and ignoring the objective law of icing of transmission lines, a transmission line icing prediction model considering the effect of transmission line tension on the bundle of icing thickness is proposed, based on a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). Firstly, the finite element calculation model of the conductor and insulator system was established, and the change rule between transmission line tension and icing thickness was studied. Then, the convolutional neural network and bidirectional gated recurrent unit were used to construct a transmission line icing thickness prediction model The model incorporated a weighted fusion of soft−dynamic time warping (Soft−DTW) and the icing change rule as the loss function. Optimal weights were determined through the utilization of the grid search algorithm and cross−validation, contributing to an enhancement of the model’s generalization capabilities and a reduction in prediction errors. The results indicate that the proposed prediction model can consider the impact of line operating parameters, avoiding the shortcomings of prediction results conflicting with actual physical laws. Compared with traditional non−mechanical models, the proposed model showed reductions in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 0.26–0.51%, 0.24–0.44%, and 5.77–13.33%, respectively, while the coefficient of determination (R2) increased by 0.07–0.13.https://www.mdpi.com/1996-1073/17/4/945icing predictionobjective law of icingdynamic time warpingbidirectional gate recurrent unitconvolutional neural networks |
| spellingShingle | Feng Wang Hongbo Lin Ziming Ma Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters Energies icing prediction objective law of icing dynamic time warping bidirectional gate recurrent unit convolutional neural networks |
| title | Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters |
| title_full | Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters |
| title_fullStr | Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters |
| title_full_unstemmed | Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters |
| title_short | Transmission Line Icing Prediction Based on Dynamic Time Warping and Conductor Operating Parameters |
| title_sort | transmission line icing prediction based on dynamic time warping and conductor operating parameters |
| topic | icing prediction objective law of icing dynamic time warping bidirectional gate recurrent unit convolutional neural networks |
| url | https://www.mdpi.com/1996-1073/17/4/945 |
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