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
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 efficien...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-09-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/9/2321 |
| _version_ | 1828147045461917696 |
|---|---|
| author | Weijun Wang Weisong Peng Xin Tan Haoyue Wang Chenjun Sun |
| author_facet | Weijun Wang Weisong Peng Xin Tan Haoyue Wang Chenjun Sun |
| author_sort | Weijun Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:54:44Z |
| format | Article |
| id | doaj.art-68f8d953967c4d53933a0458db634f40 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:54:44Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-68f8d953967c4d53933a0458db634f402022-12-22T04:03:43ZengMDPI AGEnergies1996-10732018-09-01119232110.3390/en11092321en11092321Forecasting the Low-Voltage Line Damage Caused by Typhoons in China Based on the Factor Analysis Method and an Improved Gravitational Search Algorithm-Extreme Learning MachineWeijun Wang0Weisong Peng1Xin Tan2Haoyue Wang3Chenjun Sun4Department of Economics and Management, North China Electric Power University, Baoding 071000, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071000, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071000, ChinaDepartment of Economics and Management, North China Electric Power University, Baoding 071000, ChinaHebei Electric Power Co., Ltd., Shijiazhuang 050000, ChinaThe 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.http://www.mdpi.com/1996-1073/11/9/2321typhoon destructiongrey relational analysis (GRA)factor analysisextreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM) |
| spellingShingle | Weijun Wang Weisong Peng Xin Tan Haoyue Wang Chenjun Sun 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 typhoon destruction grey relational analysis (GRA) factor analysis extreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM) |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | typhoon destruction grey relational analysis (GRA) factor analysis extreme learning machine optimized by an improved gravitational search algorithm (IGSA-ELM) |
| url | http://www.mdpi.com/1996-1073/11/9/2321 |
| work_keys_str_mv | AT weijunwang forecastingthelowvoltagelinedamagecausedbytyphoonsinchinabasedonthefactoranalysismethodandanimprovedgravitationalsearchalgorithmextremelearningmachine AT weisongpeng forecastingthelowvoltagelinedamagecausedbytyphoonsinchinabasedonthefactoranalysismethodandanimprovedgravitationalsearchalgorithmextremelearningmachine AT xintan forecastingthelowvoltagelinedamagecausedbytyphoonsinchinabasedonthefactoranalysismethodandanimprovedgravitationalsearchalgorithmextremelearningmachine AT haoyuewang forecastingthelowvoltagelinedamagecausedbytyphoonsinchinabasedonthefactoranalysismethodandanimprovedgravitationalsearchalgorithmextremelearningmachine AT chenjunsun forecastingthelowvoltagelinedamagecausedbytyphoonsinchinabasedonthefactoranalysismethodandanimprovedgravitationalsearchalgorithmextremelearningmachine |