强化学习方法对高效热光伏过器设计的强化学习方法
Paulina V Escobar1,2, Hang Wang1, Junshan Zhang1
1Department of Electrical and Computer Engineering, University of California, Davis, California 95616, United States.
深度强化学习为热光伏 (TPV) 系统设计先进的光学过器. 这种方法优化了光谱匹配,预测TPV能量转换的效率超过50%.
科学领域:
- 能源转换 能源转换
- 材料科学 材料科学 材料科学
- 光学工程是指光学工程.
背景情况:
- 热光伏 (TPV) 系统有效地将热辐射转化为电力,特别是来自废热.
- 优化TPV效率需要热源和光伏电池之间的精确光谱匹配.
- 设计用于光谱控制的多层光学过器提出了复杂的优化挑战.
研究的目的:
- 开发一个深度强化学习 (DRL) 框架,用于设计TPV系统的高性能多层光学过器.
- 为了使光伏带间隙以上的能量光子的选择性传输.
- 通过反射不需要的光子来最大限度地提高TPV系统功率转换效率.
主要方法:
- 将转移矩阵方法模拟与DRL框架集成.
- 使用定制奖励功能来指导过器设计.
- 纳入详细的平衡模型来预测系统性能.
主要成果:
- 演示DRL设计的过器,接近理想的光谱形状.
- 预测光伏电池的TPV效率超过50%.
- 在低于1500°C的发射器温度下实现高效率.
结论:
- DRL提供了一个可扩展的,数据驱动的方法来设计TPV系统中的先进光学元件.
- 开发的框架有效地解决了TPV设计中的光谱匹配挑战.
- 这种方法为下一代高效能能源转换系统铺平了道路.
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