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Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search

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  • Xiaomin Xu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Lihui Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yongli Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Keke Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people’s daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA) is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD) decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM) is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM) and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) to compare prediction accuracy. Finally, we find that these improvements facilitate the forecasting efficiency and improve the performance of the model. As a result, the proposed model obtains more ideal solutions and has higher accuracy and stronger generalization than other algorithms.

Suggested Citation

  • Xiaomin Xu & Dongxiao Niu & Lihui Zhang & Yongli Wang & Keke Wang, 2017. "Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search," Energies, MDPI, vol. 10(11), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1862-:d:118741
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    References listed on IDEAS

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    1. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
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    Cited by:

    1. Zhang, Lidong & Zhao, Yuze & Guo, Yunfeng & Hu, Tianyu & Xu, Xiandong & Zhang, Duanmei & Song, Changpeng & Guo, Yuanjun & Ma, Yuanchi, 2024. "Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm," Renewable Energy, Elsevier, vol. 237(PB).
    2. Jiazheng Lu & Jun Guo & Zhou Jian & Yihao Yang & Wenhu Tang, 2018. "Resilience Assessment and Its Enhancement in Tackling Adverse Impact of Ice Disasters for Power Transmission Systems," Energies, MDPI, vol. 11(9), pages 1-15, August.
    3. Yuebing Xu & Jing Zhang & Zuqiang Long & Yan Chen, 2018. "A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting," Energies, MDPI, vol. 11(5), pages 1-15, April.

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