Nonlinear model identification of wind turbine with a neural network
Nonlinear model identification of wind turbine with a neural network
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DOI：
10.1109/tec.2004.827715
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发表时间：
2004-08
影响因子：
4.9
通讯作者：
S. Kelouwani;K. Agbossou
中科院分区：
文献类型：
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作者：
S. Kelouwani;K. Agbossou
提出了一种基于神经网络的风力机非线性模型，用于风力机输出功率的估计。该非线性模型使用风速平均值、标准差和过去的输出功率作为输入数据。采样率为1秒的风速计提供风速数据。神经网络识别过程使用10分钟的平均速度及其标准差。使用2000年9月收集的典型本地数据进行训练，而使用2000年10月收集的数据来验证模型。最优的神经网络结构为8-5-1(8个输入，5个神经元在隐层，1个神经元在输出层)。对风力机输出功率的估计均方误差小于1%。将神经网络模型与风电功率预测中常用的随机模型进行了比较。这项工作是从平均风速估算风力机发电量的基本工具。
A nonlinear model of wind turbine based on a neural network (NN) is described for the estimation of wind turbine output power. The proposed nonlinear model uses the wind speed average, the standard deviation and the past output power as input data. An anemometer with a sampling rate of one second provides the wind speed data. The NN identification process uses a 10-min average speed with its standard deviation. The typical local data collected in September 2000 is used for the training, while those of October 2000 are used to validate the model. The optimal NN configuration is found to be 8-5-1 (8 inputs, 5 neurons on the hidden layer, one neuron on the output layer). The estimated mean square errors for the wind turbine output power are less than 1%. A comparison between the NN model and the stochastic model mostly used in the wind power prediction is done. This work is a basic tool to estimate wind turbine energy production from the average wind speed.