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在气速计塔中的多个风速传感器的层次数据融合算法.

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概括

本研究介绍了一种分层数据融合策略,用于使用多个风力计准确测量风速. 该方法显著提高了风能和气象应用的数据质量和处理效率.

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这就是Q-learning.阿奎拉优化器 (Aquila Optimizer) 是一个优化器.数据融合数据融合极端学习机器 (ELM) 是一种极端学习机器.没有香味的卡尔曼过器 (UKF)

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科学领域:

  • 工程 工程师 工程师 工程师
  • 数据科学数据科学数据科学
  • 环境科学 环境科学

背景情况:

  • 精确的风速测量对于风力发电和气象监测至关重要.
  • 来自风力发电塔上的风力计的多传感器数据融合是高精度风速数据的主要方法.
  • 现有的核聚变方法在质量和效率方面面临挑战.

研究的目的:

  • 提出一个层次化的数据融合战略,以提高多传感器风速数据融合的质量和效率.
  • 提高风力测量系统数据处理的准确性和速度.

主要方法:

  • 一种两阶段的融合方法:局部融合使用模糊逻辑和强度因子增强的无气味卡尔曼波器 (FLR-UKF) 进行无噪声和融合.
  • 全球融合使用极端学习机器 (ELM),通过Q学习改进的Aquila优化器 (QLIAO-ELM) 优化,并增强了搜索功能.

主要成果:

  • 与传统的无气味卡尔曼过器 (UKF) 相比,FLR-UKF可以将根平均平方误差 (RMSE) 降低26.46%28.6%.
  • 与标准ELM和ISSA-ELM相比,QLIAO-ELM实现了RMSE减少的27.1%和14.0%,分别是.
  • 拟议的方法证明了风速数据融合的提高准确性和效率.

结论:

  • 层次数据融合策略有效地提高了多传感器风速测量的准确性和效率.
  • 新的FLR-UKF和QLIAO-ELM方法比风力数据处理的现有技术提供了显著的改进.
  • 这种方法为可再生能源和气象学至关重要的高精度风速信息提供了可靠的解决方案.