基于强化学习的调控制用于使用双深Q网络的风力轮机
Víctor Espinoza1, Carolina Ormaza2, Christian Tutivén3
1Mechatronics Engineering, Faculty of Mechanical Engineering and Production Sciences,Escuela Superior Politécnica del Litoral, ESPOL, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, Guayaquil, 090902, Ecuador.
ISA transactions
|January 15, 2026
概括
本研究引入了风力轮调节的新增强化学习框架,显著减少了15.87%的功率波动,同时保持了结构安全. 这种先进的控制提高了风能效率.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
背景情况:
- 传统的风力轮机调节器在适应非线性风力动力学和减轻波动负载方面存在局限性.
- 比例积分 (PI) 控制器在变化风条件下保持最佳性能方面存在挑战.
研究的目的:
- 开发和评估一种基于强化学习 (RL) 的新型框架,用于增强风力轮机俯仰控制.
- 为了提高在不确定的风条件下运行的风力轮机的功率调节效率和结构负载减轻.
主要方法:
- 一个两阶段的RL方法,涉及使用双深Q网络 (DDQN) 算法转移和改进政策.
- 开发针对实际操作场景量身定制的新型奖励功能,完善最初基于PI控制器的PI控制器政策.
主要成果:
- 与行业标准的ROSCO控制器相比,拟的RL控制器表现出更高的性能.
- 在功率波动方面实现了15.87%的降低.
- 保持可比或稍微降低结构负载水平,确保运行安全.
结论:
- 强化学习提供了一种强大的方法来增强风力轮机功率调节.
- 开发的RL框架有效地提高了控制效率,并保持了结构完整性,而不会影响安全.
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