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WindPowerForecastingusingFuzzyNeuralNetworks
- 运用神经网络预测风速，并提出算法，最后作出比较，说明神经网络是预测的良好模型-The use of neural network to predict wind speed, and algorithm, and finally make a comparison on neural network model is a good forecast
ff
- 析了变速恒频双馈风力发电系统的工作原理。结合风力机特性和双馈发电机特性，说明了变速恒频运行方式下的最优风能捕获策略，并介绍了风力发电系统的滑模变结构控制、自适应控制、鲁棒控制和人工神经网络控制。总结了变速恒频风力发电系统的发展趋势。-Analysis of a Variable Speed Constant Frequency Wind Power Generation System works. Combination of wind turbine characteristics and t
APP26
- Field oriented control for doubly fed induction generator using artificial neural networks dedicated for wind energy conversion system
Wind-power-prediction-problem
- 利用新陈代谢灰色预测、样本自适应BP 神经网络和时间序列分析分别进行风电功率实时预测和日前预测，并采用熵值取权法确定组合权重，引入自控机制，构建反馈，提出组合预测法和基于时间序列的卡尔曼滤波法。研究结果表明，组合预测模型能减少各预测点较大误差的出现，而卡尔曼滤波能大幅消减原始序列的波动影响。-Use of metabolic gray forecast, sample adaptive BP neural network and time sequence analysis respective
3_1_1
- Genetic Algorithms for Optimal Reactive Power Compensation of a Power System with Wind Generators based on Artificial Neural Networks
Pitch-angle-control-in-wind-turbines-above-the-ra
- Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks
Wind-speed-prediction
- 基于最小二乘支持向量机理论,结合某风电场实测风速数据,建立了最小二乘支持向量机风速预测模型。对该风电场的风速进行了提前1h的预测,其预测的平均绝对百分比误差仅为8.55 ,预测效果比较理想。同时将文中的风速预测模型与神经网络理论、支持向量机(support vector machine,SVM)理论建立的风速预测模型进行了比较。仿真结果表明,文中所提模型在预测精度和运算速度上皆优于其他模型。 -Based on least squares support vector machine the
BP
- 采用BP神经网络的方法预测风力机发电功率-Adopt the method of BP neural network to predict wind turbine power