| 基于带灵敏度分析人工神经网络实现SVC最优配置 |
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| 引用本文： | 陈芬,匡晓红,许克明.基于带灵敏度分析人工神经网络实现SVC最优配置[J].贵州工业大学学报(自然科学版),2000,29(2):81-88. |
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| 作者姓名： | 陈芬 匡晓红 许克明 |
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| 作者单位： | 1. 贵州工业大学电工系,贵阳,550003
2. 贵州电力调通局,贵阳,550002 |
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| 基金项目： | 贵州省科技厅科研项目，黔基全计字(1996)3106， |
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| 摘 要： | 提出了在灵敏度分析法的基础上应用Hopfield模型对具有SVC的系统实现无功补偿优化配置.该方法中还增加了实用电压静稳判据,并在22节点系统上进行了验证.结果表明这一方法是可行的,有效的.可应用于电力系统.
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| 关 键 词： | 神经网络 灵敏度 SVC 电力系统 最优配置 |
Optimal Arrangement of SVC by Using Artificial Neural Network with Sensitivity Analysis |
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| CHEN Fen,KUANG Xiao-hong,XU Ke-ming.Optimal Arrangement of SVC by Using Artificial Neural Network with Sensitivity Analysis[J].Journal of Guizhou University of Technology(Natural Science Edition),2000,29(2):81-88. |
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| Authors: | CHEN Fen KUANG Xiao-hong XU Ke-ming |
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| Abstract: | This paper introduces a method for achieving optimal arrangement of reactive power with SVC by using Hopfield model on the basis of sensitivity analysis.This method involves practical critirion of voltage static stability and is tested on a 22 nodes power system.The calculation results show that the proposed method is effective and practicable to the electric power system. |
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| Keywords: | ANN hopfield model sensitivity analysis SVC optimal arrangement |
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