基于光伏系统人工神经网络的低成本最大功率点跟踪技术的实验验证
Ahmed Fathy Abouzeid1, Hadeer Eleraky2, Ahmed Kalas2
1Electrical Engineering Department, Port Said University, Port Said, 42526, Egypt. ahmed_abouzeid@eng.psu.edu.eg.
Scientific reports
|August 7, 2024
概括
与传统方法相比,在光伏系统中用于最大功率点跟踪 (MPPT) 的新数据驱动人工神经网络 (ANN) 算法提供了更快,更有效的太阳能优化.
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
- 人工智能在能源中的作用
背景情况:
- 光伏 (PV) 系统需要最大功率点跟踪 (MPPT) 来优化能源输出.
- 传统的 MPPT 算法如 Perturb and Observe (P&O) 和 Incremental Conductance (IC) 在跟踪速度和动态响应方面表现出局限性.
- 这些限制在快速变化的环境条件下尤其明显,例如太阳辐射和温度波动.
研究的目的:
- 提出和验证一个新的数据驱动的人工神经网络 (ANN) 算法用于光伏系统中的MPPT.
- 解决传统MPPT技术的缺点,特别是变量环境中的缓慢跟踪和不良动态.
- 为了提高太阳能优化效率和响应能力.
主要方法:
- 为MPPT开发数据驱动的人工神经网络 (ANN) 算法.
- 利用ANN的学习能力,使光伏操作点适应动态环境变化.
- 拟议的ANN-MPPT技术的模拟和实验验证,包括与传统方法进行比较分析.
主要成果:
- 拟议的ANN-MPPT技术与传统的P&O和IC方法相比,显示出更高的性能.
- 实现了98.16%的MPPT最大效率.
- 记录了1.3秒的最短追踪时间,表明了高动态响应能力.
结论:
- 数据驱动的ANN-MPPT方法有效地克服了传统MPPT技术的局限性.
- 它为在动态环境条件下高效快速优化太阳能发电提供了一个有前途的解决方案.
- 这些发现证实了ANN在光伏系统中先进的MPPT应用中的可行性和有效性.
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