| 基于样本修整和支持向量机算法的并网风电机组运行特性研究 |
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| 引用本文： | 王吉东,许昌,王欣,韩星星,郑源,刘德有.基于样本修整和支持向量机算法的并网风电机组运行特性研究[J].上海理工大学学报,2014,36(6):532-537. |
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| 作者姓名： | 王吉东 许昌 王欣 韩星星 郑源 刘德有 |
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| 作者单位： | 河海大学 能源与电气学院, 南京 210098;河海大学 能源与电气学院, 南京 210098;河海大学 能源与电气学院, 南京 210098;河海大学 能源与电气学院, 南京 210098;河海大学 能源与电气学院, 南京 210098;河海大学 能源与电气学院, 南京 210098 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)资助项目(2010CB227102-1);江苏省自然科学基金面上资助项目(2013-198);教育部留学回国人员科研启动基金资助项目(2012-940);江苏省六大人才高峰项目(2012-XNY-12) |
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| 摘 要： | 针对并网风力机组运行时非线性、耦合性和大惯性的特点,提出了一种基于样本修整和支持向量机算法的系统辨识方法,并通过实例将该方法与单纯的支持向量机算法、BP(back propagation)神经网络算法进行比较.结果表明,样本修整后与修整前相比,训练速度和预测精度都有明显提高,基于样本修整和支持向量机算法的辨识方法具有明显的优越性.
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| 关 键 词： | 支持向量机 BP神经网络 系统建模 风电 |
| 收稿时间： | 2013/9/13 0:00:00 |
Operating Characteristic of Wind Generation Unit Using the Method Based on Sample Modification and Support Vector Machine |
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| WANG Ji-dong,XU Chang,WANG Xin,HAN Xing-xing,ZHENG Yuan and LIU De-you.Operating Characteristic of Wind Generation Unit Using the Method Based on Sample Modification and Support Vector Machine[J].Journal of University of Shanghai For Science and Technology,2014,36(6):532-537. |
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| Authors: | WANG Ji-dong XU Chang WANG Xin HAN Xing-xing ZHENG Yuan and LIU De-you |
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| Institution: | College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China |
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| Abstract: | In view of that the operation characteristics of wind generation unit are nonlinear,coupled and with great inertia,a new system identification method based on sample modification and support vector machine (SVM) was put forward.By virtue of an application case,the proposed method of modified support vector machine was compared with the traditional SVM method and the back propagation (BP) neural network algorithm.The results indicate that,it is faster and more accurate than the initial one due to using modified samples.The study on the actual operation characteristics can provide reference to precise modeling and intelligent control of wind generation units. |
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| Keywords: | support vector machine BP neural network system modeling wind power |
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