| 基于粒子群算法优化支持向量机的输电线路覆冰预测 |
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| 引用本文： | 尹子任,苏小林. 基于粒子群算法优化支持向量机的输电线路覆冰预测[J]. 电力学报, 2014, 0(1): 6-9,61 |
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| 作者姓名： | 尹子任 苏小林 |
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| 作者单位： | 山西大学工程学院,太原030013 |
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| 基金项目： | 山西省自然科学基金项目（项目编号：2012011012-2）. |
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| 摘 要： | 分析了现有输电线路覆冰厚度预测方法中的不足,提出了一种基于粒子群算法优化支持向量机的输电线路覆冰预测。通过历史覆冰增长数据样本对支持向量机进行训练,利用训练的模型对线路覆冰厚度进行预测。同时利用粒子群优化算法对支持向量机关键参数进行优化,有效提高了覆冰厚度预测精度,为输电线路防冰提供了可靠依据。
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| 关 键 词： | 覆冰预测 粒子群优化 支持向量机 |
Icing Thickness Forecasting of Transmission Line Based on Particle Swarm Algorithm to Optimize SVM |
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| YING Zi-ren,SU Xiao-lin. Icing Thickness Forecasting of Transmission Line Based on Particle Swarm Algorithm to Optimize SVM[J]. Journal of Electric Power, 2014, 0(1): 6-9,61 |
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| Authors: | YING Zi-ren SU Xiao-lin |
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| Affiliation: | (Engineering College of Shanxi University, Taiyuan 030013, China) |
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| Abstract: | This essay analyzes the deficiency of existing transmission lines icing thickness prediction method and puts forward an icing thickness forecasting of transmission line based on particle swarm algorithm to optimize SVM. The historical ic- ing growth data samples are used to train support vector machine （SVM）, and the training model is used to forecast icing thickness of the line. At the same time particle swarm optimiza- tion algorithm is used to optimize the key parameters of SVM, effectively improving the icing thickness prediction accuracy ＆ providing a reliable basis for transmission line deicing. |
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| Keywords: | icing forecast particle swarm optimization support vector machine （SVM） |
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