在完全运行条件下基于变压器-XL的600MW超临界单元的数据驱动建模
Guolian Hou1, Tianhao Zhang1, Ting Huang1
1School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
ISA transactions
|January 10, 2025
概括
一个新的数据驱动模型提高了可再生能源集成的超临界 (SC) 单元的灵活性. 这种变压器-XL和优化算法方法准确地预测了在变量负载下SC单元的性能.
科学领域:
- 电力系统工程 电力系统工程
- 人工智能在能源中的作用
- 热力学和流体力学 热力学和流体力学
背景情况:
- 整合大规模的可再生能源需要在电网中提高灵活性.
- 超临界 (SC) 单元需要改进的峰值剃须能力,以适应可变负载.
- 由于强的合和非线性,在深峰剃须和可变负载下精确建模SC单元具有挑战性.
研究的目的:
- 为超临界 (SC) 单位开发一个精确的数据驱动模型.
- 为了提高SC单元的灵活和深度峰值剃须能力.
- 为更大规模的可再生能源整合到电网中,调整SC单元.
主要方法:
- 一个数据驱动的建模策略,结合了变压器-超长 (变压器-XL) 和一个量子混沌破碎机优化算法.
- 基于操作机制,开发了三种不同的SC单元模型 (一次通道,再循环,关闭).
- 使用变压器-XL进行全球特征提取,并改进了量子混沌破核算法,以实现最佳参数调节.
主要成果:
- 拟议的模型准确地捕捉了SC单元在一次性,再循环和关闭模式中的行为.
- SC单位模型的平均平方误差低于2.500E-03,表明了高精度.
- 数据驱动策略在使用现场数据的模拟中被证明是优越的,来自600兆瓦的SC单元.
结论:
- 开发的数据驱动模型提高了SC单元的运行效率和灵活性.
- 该模型适用于控制器设计和提高现代动力系统中SC单元的适应性.
- 这种方法通过确保电网稳定性和SC单元性能来促进可再生能源的整合.
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