Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line
The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the ici...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/256815 |
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| _version_ | 1832557069231718400 |
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| author | Peng Li Na Zhao Donghua Zhou Min Cao Jingjie Li Xinling Shi |
| author_facet | Peng Li Na Zhao Donghua Zhou Min Cao Jingjie Li Xinling Shi |
| author_sort | Peng Li |
| collection | DOAJ |
| description | The design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters. |
| format | Article |
| id | doaj-art-fb20e7e6ee0e42a6af7cdbcd9495f9a8 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fb20e7e6ee0e42a6af7cdbcd9495f9a82025-02-03T05:43:50ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/256815256815Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission LinePeng Li0Na Zhao1Donghua Zhou2Min Cao3Jingjie Li4Xinling Shi5Department of Electronic Engineering, Yunnan University, Kunming 650091, ChinaDepartment of Electronic Engineering, Yunnan University, Kunming 650091, ChinaDepartment of Automation, Tsinghua University, Beijing 100084, ChinaYunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, ChinaDepartment of Electronic Engineering, Yunnan University, Kunming 650091, ChinaDepartment of Electronic Engineering, Yunnan University, Kunming 650091, ChinaThe design of monitoring and predictive alarm systems is necessary for successful overhead power transmission line icing. Given the characteristics of complexity, nonlinearity, and fitfulness in the line icing process, a model based on a multivariable time series is presented here to predict the icing load of a transmission line. In this model, the time effects of micrometeorology parameters for the icing process have been analyzed. The phase-space reconstruction theory and machine learning method were then applied to establish the prediction model, which fully utilized the history of multivariable time series data in local monitoring systems to represent the mapping relationship between icing load and micrometeorology factors. Relevant to the characteristic of fitfulness in line icing, the simulations were carried out during the same icing process or different process to test the model’s prediction precision and robustness. According to the simulation results for the Tao-Luo-Xiong Transmission Line, this model demonstrates a good accuracy of prediction in different process, if the prediction length is less than two hours, and would be helpful for power grid departments when deciding to take action in advance to address potential icing disasters.http://dx.doi.org/10.1155/2014/256815 |
| spellingShingle | Peng Li Na Zhao Donghua Zhou Min Cao Jingjie Li Xinling Shi Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line The Scientific World Journal |
| title | Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line |
| title_full | Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line |
| title_fullStr | Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line |
| title_full_unstemmed | Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line |
| title_short | Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line |
| title_sort | multivariable time series prediction for the icing process on overhead power transmission line |
| url | http://dx.doi.org/10.1155/2014/256815 |
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