使用深度Q网络进行全球最大功率点跟踪的深度强化学习:在真实光伏系统中设计和实验
Luis Felipe Giraldo1, Jorge Felipe Gaviria2, María Isabella Torres2,1
1Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia.
Heliyon
|November 18, 2024
概括
通过使用深度Q网络 (DQN) 代理的深度增强学习 (DRL) 成功跟踪光伏系统中的全球最大功率点 (GMPP). 通过避免局部电源点,DQN代理优于传统方法,特别是在部分遮阳下.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 人工智能的人工智能
背景情况:
- 传统的最大功率点跟踪 (MPPT) 算法,如 Perturb 和 Observe (P&O) 与部分阴影条件 (PSC) 进行斗争,经常在局部最大值上卡住.
- 光伏 (PV) 系统需要高效的MPPT来最大限度地利用动态环境因素的能量.
研究的目的:
- 实验验证在光伏系统中实时MPPT的深度Q网络 (DQN) 代理的集成.
- 在均和部分遮阳条件下,将DQN代理与P&O算法的性能进行比较.
- 为基于DRL的MPPT模型建立一个强大的测试管道.
主要方法:
- 在实时MPPT的同步DC-DC巴克转换器中实现DQN代理.
- 在均和部分遮阳条件下进行实验测试.
- 对已建立的P&O算法进行比较性绩效分析.
主要成果:
- 与P&O算法相比,DQN代理在模拟中表现出优异的性能.
- 在PSC下的现实世界测试中,DQN代理实现了高达63.5%的功率提取比P&O算法,它被困在局部最大值.
- 成功建立了DRL模型的功能测试管道.
结论:
- DRL,特别是DQN,为光伏系统中先进的MPPT提供了一个有希望的方法,在具有挑战性的条件下优于传统方法.
- 实验验证证证实了DQN在实时GMPP跟踪方面的潜力,即使对模拟趋势的直接复制存在限制.
- 提供开源资源,包括硬件设计和代码,以促进进一步的研究和开发.
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