基于电场对半导体导电性影响的光伏参数模型的高级提取使用QIO算法
Ahmed S A Bayoumi1,2, Ragab A El Sehiemy3, Maged El-Kemary4,5
1Mathematical and Physics Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafr ElSheikh, 33516, Egypt.
Scientific reports
|July 4, 2024
概括
本研究引入了一种新方法,使用二次互波优化算法 (QIOA) 进行准确的光伏 (PV) 参数估计. 该方法有效地模拟可变电阻,在不同的光伏类型和条件下,在准确性和速度方面超过其他算法.
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
- 计算优化计算优化
背景情况:
- 准确的参数估计对于建模光伏 (PV) 电池和模块至关重要.
- 传统模型往往忽略了变电压电阻 (VVR) 对半导体导电性的影响.
- 现有的优化算法可能缺乏复杂的光伏参数提取的效率和稳定性.
研究的目的:
- 介绍使用二次互波优化算法 (QIOA) 进行光伏参数估计的新方法.
- 为了提高准确性,将变压电阻 (VVR) 纳入光伏模型中.
- 为了最大限度地减少PV设备的测量和建模I-V数据之间的根平均平方误差.
主要方法:
- 开发了一种包含电压依赖的串行电阻和分流电阻 (VVR) 的光伏模型.
- 采用二次插值优化算法 (QIOA) 来优化未知的光伏模型参数.
- 验证了QIOA与灰狼优化 (GWO),粒子群优化 (PSO),Salp群算法 (SSA) 和正弦共弦算法 (SCA) 相对应.
主要成果:
- 在正常辐射下,QIOA成功优化了光伏模块的11个参数,在低辐射下优化了多晶 (MCS) 模块的12个参数.
- 拟议的QIO方法实现了单晶 (SCS) 和MCS电池的最低绝对电流误差值.
- 与GWO,PSO,SSA和SCA相比,QIOA表现出优越的融合速度和稳定性.
结论:
- QIOA是一个非常有效和高效的算法,用于准确的PV参数估计,特别是在考虑VVR时.
- 拟议的方法在不同的辐射条件下提供了更准确的光伏电池行为表现.
- 对于太阳能领域的研究人员和工程师来说,QIOA是一个有希望的工具.
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