基于人工智能的太阳能跟踪控制算法应用于大型双面光伏发电厂.
José Vinícius Santos de Araújo1, Micael Praxedes de Lucena2, Ademar Virgolino da Silva Netto1
1Renewable and Alternatives Energies Center (CEAR), Electrical Engineering Department (DEE), Campus I, Federal University of Paraiba (UFPB), João Pessoa 58051-900, Brazil.
这项研究介绍了太阳能追踪器的AI算法,通过考虑天气和面板距离等因素来优化能源发电. 与传统追踪器相比,新系统显示了显著的能量增益.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 人工智能的人工智能
- 太阳能发电优化 太阳能发电优化
背景情况:
- 全球向低碳经济的转变需要太阳能技术的进步.
- 太阳能追踪器通过遵循太阳的轨迹来提高光伏 (PV) 电厂的容量.
- 优化太阳能追踪器性能需要考虑面板间距,反射率,双面面板和气候波动等变量.
研究的目的:
- 为太阳能追踪器开发和验证基于人工智能 (AI) 的算法.
- 将关键的环境和运营因素,包括天气变化和面板间距离,整合到太阳能跟踪中.
- 提高光伏系统的发电效率.
主要方法:
- 一个基于人工智能的算法被设计为动态调整太阳能跟踪器定位.
- 该算法结合了实时天气数据,并优化了太阳能电池板之间的距离.
- 在巴西东北部的现实太阳能发电厂使用双面面板验证了有效性.
主要成果:
- 在阴天,人工智能算法实现了高达7.83%的能量增长.
- 与商用太阳能追踪器相比,观察到平均能量增加约为1.2%.
- 开发的方法证明可以在全球范围内复制.
结论:
- 由人工智能驱动的太阳能跟踪算法可以显著提高能源产量.
- 考虑到天气和面板距离等动态因素对于最大限度地提高光伏电站效率至关重要.
- 拟议的算法为优化全球太阳能发电提供了一个强大的解决方案.
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