多策略改进的runge kutta优化器及其承诺估计太阳能光伏模块的模型参数
Serdar Ekinci1, Rizk M Rizk-Allah2, Davut Izci1,3,4
1Department of Computer Engineering, Batman University, Batman, 72100, Turkey.
Heliyon
|December 6, 2024
概括
一个改进的Runge Kutta (RUN) 优化器增强了使用真实电流电压 (I-V) 数据进行太阳能光伏 (PV) 模型参数估计. 这种方法提高了光伏系统的准确性和性能,通过各种案例研究和数据集进行验证.
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
- 计算优化计算优化
背景情况:
- 精确的太阳能光伏 (PV) 模型参数估计对于光伏系统性能至关重要.
- 电流到电压 (I-V) 数据分析需要先进的优化技术来准确识别参数.
- 现有的方法在准确建模复杂的光伏单元行为时可能面临挑战.
研究的目的:
- 引入多策略改进的Runge Kutta (IRUN) 优化器,以进行增强的光伏模型参数估计.
- 为了验证IRUN算法的有效性在单二极管,双二极管和光伏模块模型上.
- 为了减少错误并提高光伏系统性能预测的准确性.
主要方法:
- 对Runge Kutta (RUN) 优化器进行多策略改进的开发.
- 应用改进的RUN (IRUN) 算法来估计使用真实IV数据的光伏模型参数.
- 使用来自RTC的实验数据进行验证. 法国太阳能电池,一个工业光伏模块,以及CEC 2020测试套件.
主要成果:
- 与参数估计中的竞争方法相比,IRUN算法显示出更高的性能.
- 实现了低误差指标,包括平均绝对误差降至6.5E-04和根平均平方误差降至7.3668E-04.
- 通过工业光伏模块数据分析,验证了实际适用性和现实世界的好处.
结论:
- 拟议的IRUN算法为准确的光伏系统建模提供了一个实用和高效的工具.
- 该方法显著提高了光伏系统性能估计的准确性.
- IRUN为优化光伏参数提供了强大的解决方案,其性能优于现有技术.
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