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Multivariable time series prediction for the icing process on overhead power transmission line.

Li P, Zhao N, Zhou D, Cao M, Li J, Shi X - ScientificWorldJournal (2014)

Bottom Line: In this model, the time effects of micrometeorology parameters for the icing process have been analyzed.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China ; Department of Automation, Tsinghua University, Beijing 100084, China ; Yunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, China.

ABSTRACT
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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Related in: MedlinePlus

Filter results of qW, WP, WD, and SL.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4127214&req=5

fig4: Filter results of qW, WP, WD, and SL.

Mentions: (1) The sunlight intensity changes periodically. During the daytime, there is an obvious difference when the weather is clear or raining, but at night regardless whether the weather is clear or raining, the sunlight intensity is the same, equaling zero, which impacts the learning of BPNN. Having been filtered using the wavelets algorithm [31], the curve of sunlight is displayed in Figure 4. It holds certain relativity with the icing load: the icing begins and increases when the sunlight intensity is close to zero.


Multivariable time series prediction for the icing process on overhead power transmission line.

Li P, Zhao N, Zhou D, Cao M, Li J, Shi X - ScientificWorldJournal (2014)

Filter results of qW, WP, WD, and SL.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4127214&req=5

fig4: Filter results of qW, WP, WD, and SL.
Mentions: (1) The sunlight intensity changes periodically. During the daytime, there is an obvious difference when the weather is clear or raining, but at night regardless whether the weather is clear or raining, the sunlight intensity is the same, equaling zero, which impacts the learning of BPNN. Having been filtered using the wavelets algorithm [31], the curve of sunlight is displayed in Figure 4. It holds certain relativity with the icing load: the icing begins and increases when the sunlight intensity is close to zero.

Bottom Line: In this model, the time effects of micrometeorology parameters for the icing process have been analyzed.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic Engineering, Yunnan University, Kunming 650091, China ; Department of Automation, Tsinghua University, Beijing 100084, China ; Yunnan Electric Power Research Institute, China Southern Power Grid Corp., Kunming 650217, China.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus