为了解决当前光伏组件模型中存在的参数辨识精度低和稳定性差的问题,提出了一种基于折射学习机制的蝠鲼觅食优化算法的三二极管光伏组件参数辨识模型(RLMRFO-TDM)。该模型将差分进化机制融入到MRFO算法的种群更新环节,提高了MRFO算法的局部探索能力,并加快了MRFO算法收敛速度;引入折射学习机制改善了MRFO算法的随机性,提高了种群在搜索区域中的离散性和MRFO算法的全局搜索能力。利用基准测试函数,验证了RLMRFO算法的有效性;采用STP6-120/36和STM6-40/36两种光伏组件的数据集对RLMRFO-TDM模型的参数辨识进行性能测试,与其他模型相比,RLMRFO-TDM模型的辨识精度、稳定性以及收敛速度表现最优。
Abstract
In order to solve the problems of low parameter identification accuracy and poor stability in the current photovoltaic module model, a three-diode photovoltaic module parameter identification model (RLMRFO-TDM) based on manta ray foraging optimization algorithm and the refraction learning mechanism is proposed. The model integrates the differential evolution mechanism into the population updating link of MRFO algorithm, improves the local exploration ability of MRFO algorithm and speeds up the convergence speed of MRFO algorithm. The introduction of refraction learning mechanism improves the randomness of MRFO algorithm, the discreteness of population in the search area and the global search ability of MRFO algorithm. The benchmark function is used to verify the effectiveness of RLMRFO algorithm. The data sets of STP6-120/36 and STM6-40/36 photovoltaic modules are used to test the performance of parameter identification of RLMRFO-TDM model. Compared with other models, RLMRFO-TDM model has the best identification accuracy, stability and convergence speed.
关键词
计量学 /
光伏电池 /
参数辨识 /
折射学习 /
蝠鲼优化算法
Key words
metrology /
photovoltaic cell /
parameter identification /
refraction learning /
MRFO algorithm
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