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Energies 2018, 11(9), 2321; https://doi.org/10.3390/en11092321

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

1
Department of Economics and Management, North China Electric Power University, Baoding 071000, China
2
Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
*
Author to whom correspondence should be addressed.
Received: 2 August 2018 / Revised: 28 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
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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. View Full-Text
Keywords: typhoon destruction; grey relational analysis (GRA); factor analysis; extreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM) typhoon destruction; grey relational analysis (GRA); factor analysis; extreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, W.; Peng, W.; Tan, X.; Wang, H.; Sun, C. 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. Energies 2018, 11, 2321.

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