循环神经网络用于变速风力轮机的调度控制
Aamer Bilal Asghar1, Raza Ehsan1, Khazina Naveed2
1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore, Pakistan.
Science progress
|April 29, 2024
概括
这项研究实施了软计算技术来控制风力轮机的调,发现反复神经网络 (RNN) 最有效. 这些人工智能方法确保了变速风力轮机的稳定功率输出.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 风能是重要的可再生能源,但风速变化给轮机运行带来了挑战.
- 倾斜角控制对于调节发电机输出功率以其额定值至关重要,特别是在强风条件下.
研究的目的:
- 实施和评估软计算技术,以便在变速风力轮机中有效控制角.
- 为了比较循环神经网络 (RNN),自适应神经模糊推理系统 (ANFIS),多层感知器前神经网络 (MLPFFNN) 和模糊逻辑控制器 (FLC) 的性能.
主要方法:
- 利用MATLAB/Simulink实现和测试RNN,ANFIS,MLPFFNN和FLC在国家可再生能源实验室的5MW海上基线风力轮机数据上.
- 输入变量包括风速,尖端速度比率和功率系数;输出是俯仰角.
- 使用平均平方误差 (MSE) 和根平均平方误差 (RMSE) 评估性能.
主要成果:
- 经常性神经网络 (RNN) 在MSE (3.28e-11) 和RMSE (5.54e-06) 的最低值显示出优异的性能.
- 然后是MLPFFNN,MSE达到2.17e-10和RMSE达到1.56e-05.
- 与RNN和MLPFFNN相比,ANFIS和FLC显示出更高的错误值,表明性能不那么理想.
结论:
- 软计算技术,特别是RNN,为风力轮机转角控制提供了可靠和强大的解决方案.
- 实施的方法有效地稳定了额定值的输出功率,这对于电网集成至关重要.
- 建议的控制方案适合使用dSPACE和NI卡等硬件接口进行实际实施.
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