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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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Detalles Bibliográficos
Autores principales: Li, Peng, Zhao, Na, Zhou, Donghua, Cao, Min, Li, Jingjie, Shi, Xinling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127214/
https://www.ncbi.nlm.nih.gov/pubmed/25136653
http://dx.doi.org/10.1155/2014/256815
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author Li, Peng
Zhao, Na
Zhou, Donghua
Cao, Min
Li, Jingjie
Shi, Xinling
author_facet Li, Peng
Zhao, Na
Zhou, Donghua
Cao, Min
Li, Jingjie
Shi, Xinling
author_sort Li, Peng
collection PubMed
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.
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spelling pubmed-41272142014-08-18 Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line Li, Peng Zhao, Na Zhou, Donghua Cao, Min Li, Jingjie Shi, Xinling ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-07-17 /pmc/articles/PMC4127214/ /pubmed/25136653 http://dx.doi.org/10.1155/2014/256815 Text en Copyright © 2014 Peng Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Peng
Zhao, Na
Zhou, Donghua
Cao, Min
Li, Jingjie
Shi, Xinling
Multivariable Time Series Prediction for the Icing Process on Overhead Power Transmission Line
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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4127214/
https://www.ncbi.nlm.nih.gov/pubmed/25136653
http://dx.doi.org/10.1155/2014/256815
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