| 基于RBF神经网络的风力发电机组系统辨识研究 |
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| 引用本文： | 杨震宇,王青,魏新刚,应有,孙勇.基于RBF神经网络的风力发电机组系统辨识研究[J].机电工程,2017,34(6):639-658. |
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| 作者姓名： | 杨震宇 王青 魏新刚 应有 孙勇 |
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| 作者单位： | 1. 浙江运达风电股份有限公司,浙江 杭州,310012;2. 浙江运达风电股份有限公司,浙江 杭州310012;风力发电系统国家重点实验室,浙江 杭州310012 |
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| 基金项目： | 国家科技支撑计划资助项目 |
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| 摘 要： | Aiming at the problems of difficult to establish the accurate mathematical model of wind power generation，identification of the wind turbine based on RBF neural network was presented. The dynamic process of the torque loop and the pitch loop was simulated，RBF neural network algorithm was adopted to identification the torque loop and the pitch loop. RBF basis function was adopted to form space. If the hidden layer RBF parameters was determined， the nonlinear mapping relation was determined. The output layer was the hidden layer nodes output linear weighted summation. The result indicate that identification of torque loop，the input is torque，the output is speed，the torque loop error rate is about 1 %. Identification of pitch loop，the input is pitch angle，the output is speed，the pitch loop eror rate is about 3%. The pitch loop is a very complicated nonlinear model，the model structure is influenced by many aspects，identification result error is bigger than the torque loop identification error，but the error rate is allowed. The algorithm has higher precision and efficiency. ABSTRACT FROM AUTHOR]
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| 关 键 词： | identification radi-calbasii function (RBF) neural network the wind turbine 辨识 风力发电机组 RBF神经网络 Language of Keywords: English Chinese |
Identification of the wind turbine system based on RBF neural network |
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| YANG Zhen-yu,WANG Qing,WEI Xin-gang,YING You,SUN Yong.Identification of the wind turbine system based on RBF neural network[J].Mechanical & Electrical Engineering Magazine,2017,34(6):639-658. |
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| Authors: | YANG Zhen-yu WANG Qing WEI Xin-gang YING You SUN Yong |
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| Abstract: | Aiming at the problems of difficult to establish the accurate mathematical model of wind power generation, identification of the wind turbine based on RBF neural network was presented. The dynamic process of the torque loop and the pitch loop was simulated, RBF neural network algorithm was adopted to identification the torque loop and the pitch loop. RBF basis function was adopted to form space. If the hidden layer RBF parameters was determined, the nonlinear mapping relation was determined. The output layer was the hidden layer nodes output linear weighted summation. The result indicate that identification of torque loop, the input is torque, the output is speed, the torque loop error rate is about 1%. Identification of pitch loop, the input is pitch angle, the output is speed, the pitch loop error rate is about 3%. The pitch loop is a very complicated nonlinear model, the model structure is influenced by many aspects, identification result error is bigger than the torque loop identification error, but the error rate is allowed. The algorithm has higher precision and efficiency. |
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| Keywords: | the wind turbine radi-calbasis function( RBF) neural network identification |
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