从激光雷达测量到转子有效风速预测:经验模式分解和门式反复单元解决方案
Shuqi Shi1,2, Zongze Liu1, Xiaofei Deng3
1Hunan Provincial Key Laboratory of Grids Operation and Control on Multi-Power Sources Area, Shaoyang University, Shaoyang 422000, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究引入了一种使用激光雷达,实证模式分解 (EMD) 和封闭循环单元 (GRU) 的新方法,以准确预测风力轮机的转子有效风速 (REWS),改进控制系统.
科学领域:
- 可再生能源工程可再生能源工程
- 风力轮机技术 风力轮机技术
- 信号处理 信号处理
背景情况:
- 传统的风速传感器在多点测量方面存在问题.
- 精确预测转子有效风速 (REWS) 对于先进的风力轮控制至关重要,但缺乏足够的研究.
- 激光雷达技术为精确的REWS测量提供了潜力.
研究的目的:
- 开发一个数据驱动的框架,使用激光雷达测量来预测REWS.
- 通过准确的REWS预测来增强风力轮机的控制.
- 将拟议的EMD-GRU模型与传统方法进行比较.
主要方法:
- 使用激光雷达测量用于REWS预测.
- 使用实证模式分解 (EMD) 将激光雷达时间序列分成内在模式函数 (IMF).
- 开发了一个封闭的循环单位 (GRU) 网络,对分类的IMF进行训练,并使用平衡优化器 (EO) 结合输出.
- 提出了三个EMD-GRU方案,具有不同的输入组合,通过BLADED模拟进行验证.
主要成果:
- 与机制模型相比,EMD-GRU模型显著降低了平均绝对误差,在六个数据集中,降低范围从48.18%到65.95%.
- 证明了REWS数据驱动预测框架的有效性.
- 通过详细的模拟验证了拟议的预测方案.
结论:
- 拟议的EMD-GRU框架提供了准确的REWS预测,性能优于传统机制模型.
- 这种方法使得风力轮机的预测控制能够取得进展.
- 基于激光雷达的EMD-GRU为增强风能系统提供了强大的解决方案.
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