Forecasting the Low-Voltage Line Damage Caused by Typhoons in China Based on the Factor Analysis Method and an Improved Gravitational Search Algorithm-Extreme Learning Machine

Weijun Wang, Weisong Peng, Xin Tan, Haoyue Wang and Chenjun Sun
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Weijun Wang: Department of Economics and Management, North China Electric Power University, Baoding 071000, China
Weisong Peng: Department of Economics and Management, North China Electric Power University, Baoding 071000, China
Xin Tan: Department of Economics and Management, North China Electric Power University, Baoding 071000, China
Haoyue Wang: Department of Economics and Management, North China Electric Power University, Baoding 071000, China
Chenjun Sun: Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China

Energies, 2018, vol. 11, issue 9, 1-12

Abstract: The frequency of typhoons in China has gradually increased, resulting in serious damage to low-voltage power grid lines. Therefore, it is of great significance to study the influencing factors and predict the amount of damage, which contributes to enhancing wind resistance and improving the efficiency of repairs. In this paper, 18 influencing factors with a correlation degree higher than 0.75 are selected by grey correlation analysis, and then converted into six common factors by factor analysis. Additionally, an extreme learning machine optimized by an improved gravitational search algorithm, hereafter referred to as IGSA-ELM, is established to predict the damage caused to the low-voltage lines by typhoons and verify the effectiveness of the factor analysis. The results reveal that the six common factors generated by factor analysis can effectively improve the prediction accuracy and the fitting effect of IGSA-ELM is better than those of the extreme learning machine (ELM) and the extreme learning machine based on particle swarm optimization (PSO-ELM). Finally, this article proposes valid policy recommendations to improve the anti-typhoon capacity and repair efficiency of the low-voltage lines in Guangdong Province.

Keywords: typhoon destruction; grey relational analysis (GRA); factor analysis; extreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM) (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: 2018
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