一个自适应的蛇优化算法,结合了减去平均值的优化器,用于光伏电池的参数识别
Chunliang Mai1,2, Lixin Zhang3,4, Xue Hu1,2
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.
Heliyon
|August 21, 2024
概括
本研究引入了一种增强的蛇算法 (ISASO),用于准确的光伏 (PV) 模型参数识别. 伊萨索显著提高了估计太阳能光伏参数的准确性和可靠性,优于现有的方法.
科学领域:
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 光伏 (PV) 模型参数的识别对于高效的太阳能利用至关重要.
- 传统的方法与非线性,多个参数和局部最佳情况作斗争,阻碍了光伏系统的性能.
- 准确的参数估计是最大限度地提高太阳能转换效率和可靠性的关键.
研究的目的:
- 为准确的光伏模型参数识别开发一个先进的优化算法.
- 克服现有方法的局限性,包括低准确度,缓慢的融合,以及对局部最佳的敏感性.
- 提高全球搜索能力和人口多样性,以进行可靠的参数估计.
主要方法:
- 提出了一个增强的蛇算法 (ISASO) 集成减法平均基优化 (SABO).
- 包含帐混乱地图,以改善最初的人口质量和多样性.
- 采用动态学习因子和适应性惯性权重来加速融合.
- 在CEC2005基准函数和各种光伏模型上验证ISASO.
主要成果:
- 与现有方法相比,ISASO在光伏模型参数识别方面表现出卓越的准确性和可靠性.
- 在标准和模拟的光伏数据之间达到最低的根平均平方误差 (RMSE) 值.
- 对比分析证实了ISASO与其他元启发算法的有效性.
结论:
- 伊萨索为准确的太阳能光伏模型参数估计提供了可靠和有效的解决方案.
- 拟议的方法通过提高全球搜索和融合速度来提高优化性能.
- ISASO代表了可再生能源系统计算方法的重大进步.
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