黑龙江科技大学学报2025，Vol.35Issue(2)：337-343,7.DOI:10.3969/j.issn.2095-7262.2025.02.025
基于UKF的分数阶微分融合算法在光伏检测中的应用
Application of fractionalorder differential fusion algorithm based on UKF in photovoltaic detection
摘要
Abstract
During a photovoltaic(PV)power station in run,the temperature of the PV panels is a very important parameter.This paper aims to achieve the accurate temperature detection of the PV pan-els,and proposes a fractional-order differential operator fusion algorithm based on Unscented Kalman Fil-ter(UKF).The study involves eliminating the inherent noise errors in the sensors by unscented Kalman filtering on the data from multiple temperature sensors;and fusing the measurement data from different sensors by fractional-order differential operator.The experimental results show that this algorithm exhibits significant advantages in reducing the deviation and improving the accuracy of data fusion compared with the Kalman Filter(KF)algorithm and the UKF algorithm.The proposed algorithm is significantly better than the other two algorithms with the root mean square error of the fused sensor data by 0.021,and the mean absolute error by 0.017.关键词
光伏/UKF/分数阶微分算子/噪声误差/数据融合Key words
photovoltaic/UKF/fractional order differential operator/noise error/data fusion分类
信息技术与安全科学引用本文复制引用
左延红,耿国庆,周超,夏仕龙..基于UKF的分数阶微分融合算法在光伏检测中的应用[J].黑龙江科技大学学报,2025,35(2):337-343,7.基金项目
国家自然科学基金项目(51874005) （51874005）
安徽省教育厅重点自然科学研究项目(K201004317) （K201004317）
