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气力轮机模型的不确定性评估基于非线性自回归外源模型和蒙特卡洛脱落
Armando Cajahuaringa1, Rubén Aquize Palacios1, Juan M Mauricio Villanueva2
1Universidad Nacional de Ingeniería, Av. Tupac Amaru 210, Rimac, Lima 150101, Peru.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究开发了用于燃气轮机运行的人工神经网络模型,准确估计了旋转速度和不确定性. 这通过先进的系统识别来提高燃气轮机的可靠性和性能.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 热力学是一种热力学.
背景情况:
- 燃气轮机对于发电和工业过程至关重要.
- 优化燃气轮机运行需要准确的系统识别模型.
- 复杂的非线性系统需要先进的建模技术,如人工智能.
研究的目的:
- 开发一款燃气轮机模型,用于估计旋转速度及其相关的不确定性.
- 应用人工智能,特别是NARX和LSTM神经网络,用于系统识别.
- 通过精确的操作参数估计来提高燃气轮机的可靠性.
主要方法:
- 使用了NARX (非线性自回归与外源输入) 和LSTM (长短期记忆) 神经网络.
- 实施了蒙特卡洛脱学模拟,用于不确定性估计.
- 经过训练和验证的模型使用来自215兆瓦燃气轮机的实验数据.
主要成果:
- 实现了0.02%的平均绝对百分比误差 (MAPE) 对于旋转速度估计.
- 量化了与 2.2 RPM 的轮旋转速度估计相关的不确定性.
- 证明了基于人工智能的模型在复杂的燃气轮机系统识别中的有效性.
结论:
- 人工神经网络与蒙特卡洛掉落相结合,提供精确的燃气轮机旋转速度估计和不确定性量化.
- 开发的模型显著提高了优化燃气轮机性能和可靠性的潜力.
- 这种方法提供了一个强大的方法来分析和改进大型热电厂的运行.
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