一个有效的基于多个模型的非线性控制USC发电厂
Chuanliang Cheng1, Chen Peng1, Xiangpeng Xie2
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 201900, China.
ISA transactions
|February 4, 2024
概括
本研究引入了一种用于超超临界 (USC) 发电厂的新型非线性控制方法. 综合方法提高了能源效率,确保快速和稳定的运行,即使在显著的负载变化.
科学领域:
- 发电工程 发电工程 发电工程
- 控制系统理论 控制系统理论
- 人工智能在能源中的作用
背景情况:
- 超超临界 (USC) 炉轮机组对于高效的发电非常重要.
- 非线性和缓慢的动态响应是USC单元控制的重大挑战.
- 优化USC单位的能源效率是电力行业的一个关键问题.
研究的目的:
- 为USC炉轮机单元开发一个强大的非线性控制方法.
- 解决USC系统中非线性和缓慢动态的挑战.
- 提高发电厂的能源效率和运行稳定性.
主要方法:
- 将内部模型控制 (IMC) 和通用预测控制 (GPC) 整合到一个统一的非线性控制框架中.
- 使用长短期记忆 (LSTM) 神经网络来改善IMC组件的响应速度.
- 在非线性GPC组件中使用复合加权人类学习优化 (CWHLO) 网络来实现高精度跟踪.
主要成果:
- 基于LSTM的IMC显示了对设定点的快速趋同,显著提高了系统响应速度.
- CWHLO-GPC实现了高精度的跟踪性能.
- 在1000MW的USC发电厂进行的模拟证实了该方法在巨大的负载变化下提供快速和稳定的动态响应的能力.
结论:
- 拟议的综合IMC-GPC非线性控制方法有效优化USC炉轮机单元的能源效率.
- 这种新的方法成功地减轻了非线性和缓慢的动态,从而提高了运营性能.
- 这种控制策略为提高大规模发电系统的稳定性和效率提供了一个有希望的解决方案.
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