HMAMRL:在广泛负载运行下对燃煤发电系统进行多标准的灵活协调控制
IEEE transactions on cybernetics
|September 24, 2025
概括
本研究介绍了一种层次模型-无意识的元强化学习 (HMAMRL) 框架,用于在燃煤发电系统 (CPGS) 中灵活的宽载荷跟踪,以增强可再生能源的整合.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 能源系统 能源系统
背景情况:
- 在燃煤发电系统 (CPGS) 中,灵活和高效的宽载荷跟踪对于整合可再生能源至关重要.
- 动态特征和任务分配差异在热能发电机组的宽载荷运行过程中带来了挑战.
研究的目的:
- 提出一种新的等级模型-无学元强化学习 (HMAMRL) 框架,用于CPGS中强大的宽载荷跟踪.
- 解决热电单元运行中的动态特征和任务分配的挑战.
- 为了确保在不同负载条件下有效的概括.
主要方法:
- 一个层次模型-无意识的元强化学习 (HMAMRL) 框架,结合了内部和外部元学习.
- 一个适应性的多标准奖励功能,以平衡负载跟踪,煤炭消耗和输入波动成本.
- 一个截断的近接政策优化 (TPPO) 算法,用于在物理约束范围内精确的负载控制.
主要成果:
- 拟议的HMAMRL框架在宽载荷跟踪方面展示了有效和卓越的性能.
- 适应性奖励功能成功地平衡了多个成本标准.
- 该TPPO算法确保在运行限制内精确控制.
结论:
- 在CPGS中,HMAMRL框架为灵活的宽载荷跟踪提供了一个强大的解决方案.
- 该研究强调了超强化学习在提高电力系统灵活性和可再生能源整合方面的潜力.
- 拟议的方法在160MW和1000MW的CPGS上得到了验证.
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