摘要
针对光伏组件参数辨识问题,通过调整光伏单二极管超越方程重构低计算复杂度的目标函数,以预估计模型参数对搜索空间进行优化.然后,结合多种群粒子群算法与单纯形算法的优点,构造出N-MPSO混合新算法用于光伏组件模型参数的精确稳定辨识.最后,利用多种实际光伏组件测量数据对所提方法进行检验.结果表明:N-MPSO算法相较于传统算法能够更加准确、快速,且能稳定地辨识出任意环境条件下光伏组件的模型参数,对于光伏组件及光伏电站的设计、测试与诊断具有实际意义.
Addressing the issue of photovohaic module parameters identification, a new hybrid algorithm based on multi-group particle swarm optimization and simplex method is proposed. Firstly, the transcendental equation of the single diode photovohaic model is modified so as to greatly reduce the computation complexity. Secondly, the search space for the parameters is optimized by pre-estimating the parameters initial value. And then, combining the advantage of muhi-group particle swarm optimization and simplex method, a hybrid N-MPSO algorithm is constructed to quickly obtain the stable and accurate parameters. Finally, the algorithm is validated by several groups of I-V data measured from some typical photovohaic modules. The results show that the proposed N-MPSO algorithm can reach a higher accuracy and lower time complexity compared with some other conventional methods, which is significant to the design, testing and diagnosis of photovoltaic modules and power stations.
作者
吴越
陈志聪
吴丽君
林培杰
程树英
陆培民
WU Yue CHEN Zhicong WU Lijun LIN Peijie CHENG Shuying LU Peimin(Institute of Micro-Nano Devices ＆ Solar Cells, Fuzhou University, Fuzhou, Fujian 350116, China)
出处
《福州大学学报（自然科学版）》
CAS
北大核心
2017年第1期108-114,共7页
Journal of Fuzhou University(Natural Science Edition)
基金
国家自然科学基金资助项目(51508105
61601127)
福建省自然科学基金资助项目(2015J05124)
福建省科技厅高校产学合作资助项目(2016H6012)
福建省科技厅工业引导性重点资助项目(2015H0021)
福建省教育厅产学研资助项目(JA14038)
福建省经信委省级技术创新重点资助项目(830020)
