| 基于PSOEM优化LSSVM的接地网腐蚀预测研究 |
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| 引用本文： | 王小军,高广德,吴田,谢枭,王若昕,沈丹青,何丽娜,刘闯.基于PSOEM优化LSSVM的接地网腐蚀预测研究[J].陕西电力,2020,0(11):68-73. |
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| 作者姓名： | 王小军 高广德 吴田 谢枭 王若昕 沈丹青 何丽娜 刘闯 |
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| 作者单位： | 1. 三峡大学 电气与新能源学院， 湖北 宜昌 443002； 2. 国网荆门供电公司，湖北 荆门 448000 |
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| 摘 要： | 1. 三峡大学 电气与新能源学院， 湖北 宜昌 443002； 2. 国网荆门供电公司，湖北 荆门 448000
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| 关 键 词： | 灰色关联分析 扩展记忆粒子群 最小二乘支持向量机 接地网 腐蚀 |
Research on Corrosion Prediction of Grounding Grid Based on PSOEM-optimized LSSVM |
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| WANG Xiaojun,GAO Guangde,WU Tian,XIE Xiao,WANG Ruoxin,SHEN Danqing,HE Lina,LIU Chuang.Research on Corrosion Prediction of Grounding Grid Based on PSOEM-optimized LSSVM[J].Shanxi Electric Power,2020,0(11):68-73. |
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| Authors: | WANG Xiaojun GAO Guangde WU Tian XIE Xiao WANG Ruoxin SHEN Danqing HE Lina LIU Chuang |
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| Affiliation: | 1. Colloge of Electrical Engineering＆New Energy，China Three Gorges University，Yichang 443002，China；
2. State Grid Jingmen Power Supply Company， Jingmen 448000，China |
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| Abstract: | The data of soil composition and the corrosion rate of metal sheet in 60 groups of 110 kV substation is obtained through
corrosion test. These sample data are analyzed by grey relation， showing that Cl-content， pH value， water content and salt
content is the main reasons for the corrosion of grounding grid，based on which support vectors are selected for a prediction
model. In order to improve the prediction accuracy of LSSVM model， particle swarm optimization with extended memory is used to
optimize the penalty factor and kernel function parameters of the LSSVM. The corrosion prediction model of the grounding grid
based on PSOEM-optimized LSSVM is established， and the simulation is done with the test data. The results show that the PSOEM-
LSSVM model is better in training and fitting and prediction by extrapolation. |
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| Keywords: | grey relational analysis particle swarm with extended memory least squares support vector machine grounding grid corrosion |
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