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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

The process of modeling and prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig3: The process of modeling and prediction.

Mentions: As Φ1(·) is multivariate and nonlinear, we can obtain it using BPNN (back propagation neural network) [26–28], SVM (support vector machine) [29, 30], and other data-driven methods. Here is founded by BPNN; it is a machine learning process, as shown in Figure 3. After training, that is also the prediction model of the icing load time series.


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)

The process of modeling and prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: The process of modeling and prediction.
Mentions: As Φ1(·) is multivariate and nonlinear, we can obtain it using BPNN (back propagation neural network) [26–28], SVM (support vector machine) [29, 30], and other data-driven methods. Here is founded by BPNN; it is a machine learning process, as shown in Figure 3. After training, that is also the prediction model of the icing load time series.

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