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

Weijun Wang, Dan Zhao, Liguo Fan and Yulong Jia
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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

Energies, 2019, vol. 12, issue 11, 1-21

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.

Keywords: icing thickness; extreme learning machine; ensemble empirical mode decomposition; chaotic grey wolf optimization; random forest; feature selection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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