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神经网络预测控制器基于对超超临界单元的改进的TPA-LSTM模型

Boyu Ping¹, Deliang Zeng¹, Yong Hu¹

¹North China Electric Power University, Beijing, 102206, China.

|July 15, 2024

概括

这项研究引入了一种改进的神经网络预测控制器,用于燃煤发电机组,以提高可再生能源的电网稳定性. 控制器有效地管理负载控制挑战,确保可靠的发电.

科学领域:

* 动力系统工程 * 动力系统工程
* * 控制理论控制理论
* 人工智能 * 人工智能

背景情况:

*将大规模的可再生能源整合到中国国家电网中,需要提高燃煤热电单元的灵活峰值能力.
* 煤炭发电机组现有的协调控制系统在多变量合,反应缓慢和煤炭质量不确定性方面扎.
*传统的线性预测控制方法在处理干扰不确定性方面存在局限性.

研究的目的:

* 开发一个先进的神经网络预测控制器,以提高燃煤发电机组的灵活峰值能力.
* 解决多变量合,响应速度和单元负载控制中的参数不确定性方面的挑战.
* 提高控制系统在不同操作条件下的适应性和稳定性.

主要方法:

* 建立一个数据驱动的控制模型,利用一个改进的TPA-LSTM神经网络.
*设计一个多变量协调控制策略,利用神经网络控制器进行参数解.
* 集成了用于实时重新校准的自动模型更新机制.

主要成果:

* 拟议的控制器有效地处理干扰的不确定性,超过传统的线性预测控制.
*多变量协调控制策略实现了有效的脱和在所有负载条件下高度适应性.
* 模拟结果证实了该战略对1000兆瓦超超临界单元的良好控制效率.
*自动更新模型机制在模型不匹配后显著改善了控制性能.

结论:

*神经网络预测控制器为增强燃煤发电机组灵活峰值能力提供了可行的解决方案.
* 数据驱动的方法与自动模型更新增强了控制的稳定性和适应性,这对电网稳定性至关重要.
* 这一战略通过确保可靠的动力单元运行,有效地满足了整合可再生能源的需求.

关键词:

协调控制系统-1 协调控制系统-1 数据驱动模型-4数据驱动模型-4 多步预测算法-2 多步预测算法-2 非线性预测控制器-3

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