[1]梁仕雄,侯北平.融合中值滤波与光流算法的云团跟踪算法[J].浙江科技学院学报,2023,(06):527-533.[doi:10.3969/j.issn.1671-8798.2023.06.008 ]
　LIANG Shixiong,HOU Beiping.Research on cloud tracking by fusion of median filtering and optical flow algorithm[J].,2023,(06):527-533.[doi:10.3969/j.issn.1671-8798.2023.06.008 ]
点击复制

融合中值滤波与光流算法的云团跟踪算法(/HTML)
分享到：

《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2023年06期
页码:
527-533
栏目:
出版日期:
2024-01-01

文章信息/Info

Title:
Research on cloud tracking by fusion of median filtering and optical flow algorithm
文章编号:
1671-8798(2023)06-0527-07
作者:
梁仕雄; 侯北平
(浙江科技学院 自动化与电气工程学院,杭州 310023)
Author(s):
LIANG Shixiong;  HOU Beiping
(School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
云团检测;  图像滤波;  光流算法;  云团定位;  目标跟踪
分类号:
TP391.41
DOI:
10.3969/j.issn.1671-8798.2023.06.008
文献标志码:
A
摘要:
【目的】针对现有的云团跟踪算法存在跟踪速率低、云团滤波边缘信息丢失、云团跟踪定位不准等问题,结合优化的中值滤波算法,提出一种融合中值滤波与光流算法的云团跟踪算法。【方法】首先对太阳的位置进行定位,采用红蓝阈值比分割图像,用优化的中值滤波算法滤除噪点后,检测云块及其质心坐标; 然后对云图序列进行光流处理,实现对云块的精确检测与实时跟踪; 最后在不同云量情况下进行云团跟踪试验,并且与传统光流算法、块匹配算法、ViBe(visual background extractor,视图背景提取)算法进行比较。【结果】本文算法在云团边缘检测和云团实时跟踪上表现优越,目标跟踪效率比其他3种算法的平均值提高了约6.32百分点。【结论】本研究结果有效解决了云团滤波导致边缘信息缺失的问题,对预测短期太阳辐照度的准确性有重要意义。

参考文献/References:

[1] 魏景东,赵增海,郭雁珩,等.2021年中国光伏发电发展现状与展望[J].水力发电,2022,48(10):3.
[2] NITHYA C, PREETHA J R. Development of grid-side converter-based FRT control and protection in a grid-connected solarphotovoltaic park system under different control modes[J].Frontiers in Energy Research,2022,15(10):3.
[3] BONAH E A, USMAN M, SALAH K, et al. Technical performance prediction and employment potential of solar PV systems in cold countries[J].Sustainability,2022,14(6):3546.
[4] DHASS A D, BEEMKUMAR N, HARIKRISHNAN S, et al. A review on factors influencing the mismatch losses in solar photovoltaic system[J].International Journal of Photoenergy,2022,2022:615.
[5] CUI Y, SHAO M Z, ZHOU X Q, et al. Research on supporting mechanism of ancillary service of PV system to grid energy efficiency based on multi-time and space-time operation[J].Plosone,2022,17(5):173.
[6] BOSCH J L, KLEISSL J. Cloud motion vectors from a network of ground sensors in a solar power plant[J].Solar Energy,2013,95(18):5.
[7] GUMAR A K, DEMIR F. Solar photovoltaic power estimation using meta-optimized neural networks[J].Energies,2022,15(22):8669.
[8] HOU T, WANG S. Research on the MPPT of photovoltaic power generation based on the CSA-INC algorithm[J].Energy Engineering,2022,120(1):90.
[9] NING L, ZHANG C J, DENG W, et al. A boost converter-based MPPT steady-state oscillation-free fast tracking control strategy for photovoltaic power systems[J].IET Power Electronics,2022,16(3):485.
[10] YANG H, KURTZ B, NGUYEN D, et al. Solar irradiance forecasting using a ground-based sky imager developed at UC San Diego[J].Solar energy,2014,103:502.
[11] BELLINGUER K, GIRARD R, BONTRON G, et al. A generic methodology to efficiently integrate weather information in short-term photovoltaic generation forecasting models[J].Solar Energy,2022,244:413.
[12] CHENG H Y. Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting[J].Renewable energy,2017,104:284.
[13] 赵高长,张磊,武风波.改进的中值滤波算法在图像去噪中的应用[J].应用光学,2011,32(4):678.
[14] 杨明,宋丽华.改进的快速中值滤波算法在图像去噪中的应用[J].测绘工程,2011,20(3):65.
[15] 张铮,徐超,任淑霞,等.数字图像处理与机器视觉[M].北京:人民邮电出版社:2014:213-215.
[16] WANG S H, ZHOU Y J, BAI F L, et al. Improved ViBe algorithm based on multi-frame combined with adaptive threshold[C]//Journal of Physics:Conference Series. LONDON:IOP Publishing,2022,2303(1):1.
[17] BETKA A, TERKI N, TOUMI A, et al. A new block matching algorithm based on stochastic fractal search[J].Applied Intelligence,2019,49(3):1146.

备注/Memo

备注/Memo:
收稿日期:2022-09-23
基金项目:浙江省重点研发计划项目(2021C04030)
通信作者:侯北平(1976— ),男,山东省日照人,教授,博士,主要从事图像处理、机器视觉研究。E-mail:bphou@zust.edu.cn。
更新日期/Last Update: 2023-12-31