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在动态天气条件下,通过混合GEP ANFIS MPPT提高太阳能效率

Mutiu Shola Bakare¹, Abubakar Abdulkarim², Aliyu Nuhu Shuaibu²

¹Department of Electrical Engineering, Kampala International University, Ishaka, 20000, Uganda. shola.bakare@studwc.kiu.ac.ug.

Scientific reports

|February 18, 2025

概括

研究人员使用一种新的基因表达编程-自适应神经模糊推理系统 (GEP-ANFIS) 方法优化了太阳能发电系统. 这种方法显著提高了最大功率点跟踪 (MPPT) 的效率,在高太阳辐射下达到99.84%.

科学领域:

可再生能源可再生能源是可再生能源.
电气工程电气工程
人工智能的人工智能

背景情况:

气候变化需要优化太阳能发电系统.
最大功率点跟踪 (MPPT) 对于太阳能发电效率至关重要.
适应神经模糊推理系统 (ANFIS) 提供响应性,但需要精确的训练数据.

研究的目的:

为太阳能发电开发一个优化的MPPT系统.
使用基因表达编程 (GEP) 提高基于ANFIS的MPPT的效率.
在不同的太阳条件下评估GEP-ANFIS组合模型的性能.

主要方法:

开发了一种结合ANFIS和GEP的新方法.
GEP-ANFIS模型是使用太阳辐射和温度作为输入参数进行训练的.
模拟使用Matlab/Simulink在增压转换器上进行.

主要成果:

在高太阳辐射下,GEP-ANFIS双二极管模型展示了99.84%的异常效率.
提出的方法有效地优化了参考最大输出功率.
该系统在模拟波动的太阳辐射条件下表现出强大的性能.

结论:

GEP-ANFIS方法显著提高了太阳能效率和MPPT性能.
这种新型的集成具有很大的潜力,可以在各种环境中推进太阳能利用.
该研究强调了人工智能驱动技术在优化可再生能源系统方面的有效性.

关键词:

安菲斯 (ANFIS) 是一个字母.提升转换器的增强转换器动态的天气和负载.在GEP中,GEP是GEP.在MPPTT中,MPPT是MPPT,MPPT是MPPT.太阳能PV 太阳能PV 太阳能PV

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