摘要
针对太阳能电池软退化模式下的寿命预测难度大,准确度不高等问题,提出一种先利用太赫兹光谱仪获取太阳能电池板光谱,再用基于布谷鸟算法改进的粒子群-支持向量机回归(PSO-SVR)算法预测其剩余寿命的新方法。利用紫外加速试验对预测结果进行验证对比,结果表明,该方法可用于预测不同损耗程度的太阳能电池的剩余寿命,在传统硅太阳能电池板和砷化镓太阳能电池的寿命预测上,相较于其他算法有更好的表现,其准确度分别高达98.92%和92.86%。
Aiming for the low accuracy and difficulty of predicting solar cell life by using soft failure mode,a new method is proposed to obtain solar panel spectrum by using terahertz spectrometer.Based on the cuckoo algorithm,the study predicts the cell's remaining life by applying Particle Swarm Optimization-Support Vector Regression(PSO-SVR)algorithm and finally employs the ultraviolet acceleration test to verify the prediction results.It turns out that the method is applicable to predict the remaining life of solar cells with different levels of loss.Compared with other algorithms,the technique works better on the life prediction of traditional silicon solar panels and GaAs solar cells,and the accuracies are up to 98.92%and 92.86%respectively.
作者
周兴
朱希安
王占刚
ZHOU Xing;ZHU Xi’an;WANG Zhangang(School of Telecommunications Engineering,Beijing Information Science and Technology University,Beijing 100101,China)
出处
《太赫兹科学与电子信息学报》
北大核心
2020年第3期374-379,共6页
Journal of Terahertz Science and Electronic Information Technology
基金
北京市科技创新服务能力建设基本科研业务费资助项目(市级)(科研类)(PXM2019_014224_000026)
北京市科技创新服务能力建设提升计划资助项目(PXM2017_014224_000009)
2018年促进高校内涵发展资助项目。
关键词
太阳能电池
太赫兹光谱
粒子群优化
软退化模式
solar cell
terahertz spectroscopy
Particle Swarm Optimization
soft failure mode
