Neural network compensation control for output power optimization of wind energy conversion system based on data-driven control
Neural network compensation control for output power optimization of wind energy conversion system based on data-driven control
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DOI：
10.1155/2012/736586
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发表时间：
2012
影响因子：
1.7
通讯作者：
T. Li;A. Feng;L. Zhao
中科院分区：
文献类型：
--
作者：
T. Li;A. Feng;L. Zhao
由于风的不确定性和风能转换系统的强非线性特性，建立精确的风能转换系统模型是困难的。为了解决这一问题，选择了数据驱动控制技术，基于马尔可夫模型设计了风力发电系统的数据驱动控制器。基于数据驱动的控制系统模型，设计神经网络对系统输出进行优化。为了提高神经网络的训练效率，比较了三种不同的学习规则。分析结果与风电场SCADA数据对比表明，该方法有效地减小了发电机转速波动，提高了风电机组的安全性，提高了WECS输出的准确性，捕获了更多的风能。
Due to the uncertainty of wind and because wind energy conversion systems (WECSs) have strong nonlinear characteristics, accurate model of the WECS is difficult to be built. To solve this problem, data-driven control technology is selected and datadriven controller for the WECS is designed based on the Markov model. The neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured.