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Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

Author

Listed:
  • Weijun Wang

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Dan Zhao

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Liguo Fan

    (Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

  • Yulong Jia

    (Power System and Automation, North China Electric Power University, 689 Huadian Road, Baoding 071000, China)

Abstract

The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in ‘hunan’ province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.

Suggested Citation

  • Weijun Wang & Dan Zhao & Liguo Fan & Yulong Jia, 2019. "Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine," Energies, MDPI, vol. 12(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2163-:d:237661
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    References listed on IDEAS

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    1. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
    2. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    3. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

    1. Yanpeng Hao & Zhaohong Yao & Junke Wang & Hao Li & Ruihai Li & Lin Yang & Wei Liang, 2019. "A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering," Energies, MDPI, vol. 12(24), pages 1-14, December.

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