利用基于对立的学习来进行太阳能光伏模型参数估计,使用指数分布优化算法
Nandhini Kullampalayam Murugaiyan1, Kumar Chandrasekaran2, Premkumar Manoharan3
1Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamil Nadu, 638401, India. kmsrnandhu@gmail.com.
本研究介绍了一种改进的基于对立的指数分布优化器 (OBEDO),用于光伏 (PV) 参数提取. OBEDO算法提高了估计光伏模型参数的准确性和效率,克服了传统方法的局限性.
科学领域:
- 可再生能源工程可再生能源工程
- 计算智能是一种计算智能.
- 电气工程 电气工程
背景情况:
- 由于多模型和非线性特征,光伏 (PV) 模型的参数提取是复杂的.
- 传统的算法通常由于局部最佳值而失败,并且需要大量的计算资源.
- 精确的光伏参数估计对于优化光伏系统性能和能源生产至关重要.
研究的目的:
- 为光伏 (PV) 参数提取提供一个改进的算法.
- 解决传统方法的局限性,包括局部最佳陷和高计算成本.
- 为了提高光伏模型参数识别的准确性,可靠性和效率.
主要方法:
- 基于对立的指数分布优化器 (OBEDO) 的开发和应用.
- 将基于对立的学习纳入OBEDO以加强勘探和开发.
- 对各种光伏模型 (单二极管,双二极管,三二极管,模块) 的最新算法进行严格验证.
主要成果:
- 拟议的OBEDO算法与现有方法相比,表现出优越的性能.
- 在参数估计中,OBEDO显示了增强的收速度,可靠性和准确性.
- 通过实际,统计结果和几个案例研究的验证证实了OBEDO的有效性.
结论:
- OBEDO算法是用于光伏模型参数识别的强大且计算效率高的解决方案.
- 在光伏参数提取中,OBEDO有效地减轻了局部最佳陷的风险.
- 拟议的方法为提高光伏系统的整体性能做出了重大贡献.
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