在双二极管太阳能电池模型中对修改后的火优化器进行评估,以进行新的修改
Mokhtar Said1, Alaa A K Ismaeel2, Ali M El-Rifaie3
1Electrical Engineering Department, Faculty of Engineering, Fayoum University, Faiyum, Egypt.
Scientific reports
|December 3, 2024
概括
本研究介绍了一种修改的双二极管太阳能电池模型 (NMDDSCM) 和修改的火优化器 (mFHO),用于准确的光伏参数提取. 该NMDDSCM在传统模式上表现出卓越的性能,展示了其对新能源系统的潜力.
科学领域:
- 可再生能源系统可再生能源系统
- 电气工程 电气工程
- 计算智能是一种计算智能.
背景情况:
- 光伏 (PV) 模型参数评估对于新兴能源系统至关重要.
- 由于不可预测的非线性光伏输出变化,传统的线性预测方法不足.
- 超启发式算法擅长从太阳能电池模型中提取特征.
研究的目的:
- 为了分析一种新的修饰双二极管太阳能电池模型 (NMDDSCM).
- 评估NMDDSCM与传统的双二极管太阳能电池模型 (TDDSCM) 的性能.
- 应用修改后的火优化器 (mFHO) 进行准确的光伏参数提取.
主要方法:
- 使用了修改后的火优化器 (mFHO),采用了两个增强策略.
- 参数提取被定义为一个优化问题,最大限度地减少根平均平方误差 (RMSE).
- 使用真实太阳能电池数据 (法国R.T.C) 和CEC-2022测试套件验证了性能.
主要成果:
- 该mFHO算法实现了TDDSCM的RMSE值为0.000983634,而NMDDSCM的RMSE值为0.000982485.
- 在所有经过测试的竞争对手算法中,NMDDSCM的表现始终优于TDDSCM.
- mFHO的有效性与TLBO,GWO,FHO,MFO,HBO和CHOA进行了比较.
结论:
- 拟议的NMDDSCM显示了光伏参数估计的增强精度.
- 在优化太阳能电池模型方面,mFHO算法被证明是有效和优越的.
- 这些发现支持NMDDSCM适用于先进的新能源动力系统的适用性.
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