抗弦-弦原子搜索优化 (ASCASO):一种用于对光伏模型参数估计的新方法
Wei Zhou1, Pengjun Wang2, Xuehua Zhao3
1College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, China.
一种新的优化方法,反正弦-弦算法 (ASCASO),准确地估计了光伏 (PV) 模型的参数. 这种方法通过提高光伏模型的准确性和可靠性来提高太阳能转换效率.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 计算智能是一种计算智能.
- 材料科学 材料科学 材料科学
背景情况:
- 太阳能对电力电网至关重要.
- 提高太阳能电池效率是一个关键的研究领域.
- 精确的光伏 (PV) 模型参数估计对于性能优化至关重要.
研究的目的:
- 为了引入一种新的优化算法,增强原子搜索优化 (ASO) 与反正弦-共弦机制 (ASCASO).
- 应用ASCASO来精确估计各种光伏 (PV) 模型的参数.
- 评估ASCASO的性能与现有算法对比.
主要方法:
- 在原子搜索优化 (ASO) 中整合了一个新的反弦-共弦机制,开发了ASCASO.
- 反正弦-弦机制利用正弦和正弦函数进行增强的原子位置更新.
- 在不同的光伏模型上测试ASCASO,包括R.T.C法国,Photowat-PWP201,STM6-40/36和STM6-120/36.
主要成果:
- 与其他九种算法相比,ASCASO在参数估计方面表现出卓越的准确性和可靠性.
- 拟议的方法有效估计了多个光伏模型的参数,包括单晶和多晶类型.
- 反子-子机制促进了随机的人口交换,并改善了信息交换.
结论:
- 阿斯卡索是一种高效和可靠的方法,用于准确的光伏 (PV) 模型参数估计.
- 这种新的优化方法有助于提高太阳能转换效率.
- 阿斯卡索显示了推动太阳能发电技术研究的巨大潜力.
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