| 用人工神经元网络和最小二乘法估计负荷模型的比较 |
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| 引用本文： | 邱晓燕,李兴源,宋永华,刘红超,刘俊勇.用人工神经元网络和最小二乘法估计负荷模型的比较[J].电力系统自动化,1996,20(5):29-32. |
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| 作者姓名： | 邱晓燕 李兴源 宋永华 刘红超 刘俊勇 |
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| 作者单位： | (四川联合大学电力系·610065·成都) |
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| 摘 要： | 该文分别采用现代辨识技术之一的递推最小二乘法和人工神经元网络(ANN)误差反向传播算 法(BP算法)估计2000年四川电网某一变电站的负荷模型，结果表明人工神经元网络模型 能更好地反映负荷的非线性特性。
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| 关 键 词： | 负荷模型 人工神经元网络 最小二乘辨识 |
| 收稿时间： | 1/1/1900 12:00:00 AM |
| 修稿时间： | 1/1/1900 12:00:00 AM |
COMPARISON OF LOAD MODELS USING ARTIFICIAL NEURAL NETWORKSAND RECURSIVE LEAST-SQUARES IDENTIFICATION |
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| Qiu Xiaoyan, Li Xingyuan, Song Yonghua, Liu Hongchao, Liu Junyong.COMPARISON OF LOAD MODELS USING ARTIFICIAL NEURAL NETWORKSAND RECURSIVE LEAST-SQUARES IDENTIFICATION[J].Automation of Electric Power Systems,1996,20(5):29-32. |
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| Authors: | Qiu Xiaoyan Li Xingyuan Song Yonghua Liu Hongchao Liu Junyong |
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| Abstract: | it is important to develop accurate load models for the analysis of power systemperformance. The load model of a substation in Sichunan power system has been obtained usingArtificial Neural Networks (ANN) and recursive least-squares (RLS) identification in this paper.The results show that the ANN can map the nonlinear characteristics of dynamic loads betterthan the traditional identification methods. |
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| Keywords: | load model artificial neural networks recursive least-squares identification |
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