RESEARCH ON FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAYS BASED ON ISSA-RF ALGORITHM

Xu Guimin; Song Yuhang; Xiangli Mengqiao; Yang Yalong; Duan Chendong

doi:10.19912/j.0254-0096.tynxb.2024-1808

PDF(1769 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 111-121. DOI: 10.19912/j.0254-0096.tynxb.2024-1808

RESEARCH ON FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAYS BASED ON ISSA-RF ALGORITHM

Xu Guimin¹, Song Yuhang¹, Xiangli Mengqiao², Yang Yalong^3,4, Duan Chendong¹

Author information +

History +

Abstract

This paper presents a method based on an improved sparrow search algorithm （ISSA） and optimized random forest （RF） model, aiming to improve the accuracy of fault diagnosis in photovoltaic arrays. Firstly, the photovoltaic fault data set is constructed by building a photovoltaic array to simulate five fault conditions and extracting fault vectors. Secondly, the optimization comparison among sparrow search algorithm （ISSA）, grey wolf optimization （GWO）, particle swarm optimization （PSO） and ISSA by test function is performed. The results indicate that ISSA is better than other algorithms in terms of mean value and standard deviation, showing better robustness. Then, the performance of ISSA-RF is analyzed using the simulation data set of photovoltaic array fault. It can be found that the overall accuracy of ISSA-RF reaches 97.06%, which is 6.94 percentage points higher than the traditional RF model. Finally, the data set under five different working conditions including open circuit, short circuit, shade, aging and normal operation are collected for testing ISSA-RF. The research outcomes prove that this model has high classification efficiency and accuracy, and its performance is superior to other models.

Key words

photovoltaic arrays / fault diagnosis / improved sparrow search algorithm / random forest algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Xu Guimin, Song Yuhang, Xiangli Mengqiao, Yang Yalong, Duan Chendong. RESEARCH ON FAULT DIAGNOSIS METHOD OF PHOTOVOLTAIC ARRAYS BASED ON ISSA-RF ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 111-121 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1808

References

[1] 王灿, 张羽, 田福银, 等. 基于双向主从博弈的储能电站与综合能源系统经济运行策略[J]. 电工技术学报, 2023, 38(13): 3436-3446, 3472.
WANG C, ZHANG Y, TIAN F Y, et al.Economic operation of energy storage power stations and integrated energy systems based on bidirectional master-slave game[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3436-3446, 3472.
[2] 顾崇寅, 徐潇源, 王梦圆, 等. 基于CatBoost算法的光伏阵列故障诊断方法[J]. 电力系统自动化, 2023, 47(2): 105-114.
GU C Y, XU X Y, WANG M Y, et al.CatBoost algorithm based fault diagnosis method for photovoltaic arrays[J]. Automation of electric power systems, 2023, 47(2): 105-114.
[3] 范钧玮, 饶全瑞, 赵薇, 等. 改进的YOLOv5双影像光伏故障小目标检测[J]. 太阳能学报, 2024, 45(7): 510-516.
FAN J W, RAO Q R, ZHAO W, et al.Improved YOLOv5 dual-image photovoltaic fault small target detection[J]. Acta energiae solaris sinica, 2024, 45(7): 510-516.
[4] 叶进, 卢泉, 王钰淞, 等. 基于级联随机森林的光伏故障诊断模型研究[J]. 太阳能学报, 2021, 42(3): 358-362.
YE J, LU Q, WANG Y S, et al.Research on PV fault diagnosis model based on cascaded random forest[J]. Acta energiae solaris sinica, 2021, 42(3): 358-362.
[5] 韩茂林, 杨琛, 牛锋杰, 等. 基于特征提取与改进POA的光伏阵列故障诊断[J]. 电子测量与仪器学报, 2025, 39(4): 258-269.
HAN M L, YANG C, NIU F J, et al.Photovoltaic array fault diagnosis based on feature extraction and improved pelican optimization algorithm[J]. Journal of electronic measurement and instrumentation, 2025, 39(4): 258-269.
[6] 王小宇, 刘波, 孙凯, 等. 光伏阵列故障诊断技术综述[J]. 电工技术学报, 2024, 39(20): 6526-6543.
WANG X Y, LIU B, SUN K, et al.A review of photovoltaic array fault diagnosis technology[J]. Transactions of China Electrotechnical Society, 2024, 39(20): 6526-6543.
[7] 刘恒, 马铭遥, 张志祥, 等. 热斑晶硅光伏组件I-V曲线分析及特征模拟研究[J]. 太阳能学报, 2021, 42(4): 239-246.
LIU H, MA M Y, ZHANG Z X, et al.Study on I-V curve analysis and characteristic simulation of silicon photovoltaic module with hot spot[J]. Acta energiae solaris sinica, 2021, 42(4): 239-246.
[8] 陈宇航, 闫腾飞, 谢添, 等. 基于功率损失和U-I特性综合考虑的光伏组件故障诊断方法[J]. 电机与控制应用, 2016, 43(11): 92-97.
CHEN Y H, YAN T F, XIE T, et al.A novel fault diagnosis method of photo voltaic module based on power loss and U-I characteristics[J]. Electric machines & control application, 2016, 43(11): 92-97.
[9] 蒋琳, 苏建徽, 李欣, 等. 基于可见光和红外热图像融合的光伏阵列热斑检测方法[J]. 太阳能学报, 2022, 43(1): 393-397.
JIANG L, SU J H, LI X, et al.Hot spot detection of photovoltaic array based on fusion of visible and infrared thermal images[J]. Acta energiae solaris sinica, 2022, 43(1): 393-397.
[10] 毛峡, 石天朋. 光伏热斑图像有效区域分割算法研究[J]. 太阳能学报, 2018, 39(5): 1270-1276.
MAO X, SHI T P.Research on segmentation algorithm of effective region in photovoltaic hot spot image[J]. Acta energiae solaris sinica, 2018, 39(5): 1270-1276.
[11] CHEN S Q, YANG G J, GAO W, et al.Photovoltaic fault diagnosis via semisupervised ladder network with string voltage and current measures[J]. IEEE journal of photovoltaics, 2021, 11(1): 219-231.
[12] 谢琳琳, 朱武, 崔昊杨. 改进遗传优化神经网络的光伏阵列故障诊断[J]. 电源技术, 2022, 46(7): 802-806.
XIE L L, ZHU W, CUI H Y.BP netural network based on improved genetic algorithm for fault diagnosis of photovoltaic array[J]. Chinese journal of power sources, 2022, 46(7): 802-806.
[13] 李光辉, 段晨东, 武珊. 基于半监督机器学习法的光伏阵列故障诊断[J]. 电网技术, 2020, 44(5): 1908-1913.
LI G H, DUAN C D, WU S.Fault diagnosis of PV array based on semi-supervised machine learning[J]. Power system technology, 2020, 44(5): 1908-1913.
[14] ET-TALEBY A, BOUSSETTA M, BENSLIMANE M.Faults detection for photovoltaic field based on K-means, elbow, and average silhouette techniques through the segmentation of a thermal image[J]. International journal of photoenergy, 2020, 2020(1): 6617597.
[15] 杨晓燕, 谢满承, 郭小璇, 等. 基于改进原子轨道搜索算法优化随机森林分类器的光伏系统故障诊断[J]. 综合智慧能源, 2023, 45(10): 53-60.
YANG X Y, XIE M C, GUO X X, et al.PV system fault diagnosis based on random forest classifier optimized by improved atomic orbital search algorithm[J]. Integrated intelligent energy, 2023, 45(10): 53-60.
[16] ANDERSON A J.Photovoltaic translation equations: a new approach. Final subcontract report[R]. Colorado, National Renewable Energy Laboratory, 1996.
[17] GHAREHCHOPOGH F S, NAMAZI M, EBRAHIMI L, et al.Advances in sparrow search algorithm: a comprehensive survey[J]. Archives of computational methods in engineering, 2023, 30(1): 427-455.
[18] XUE J K, SHEN B.A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems science & control engineering, 2020, 8(1): 22-34.
[19] 杨海东, 鄂加强. 自适应变尺度混沌免疫优化算法及其应用[J]. 控制理论与应用, 2009, 26(10): 1069-1074.
YANG H D, E J Q. An adaptive chaos immune optimization algorithm with mutative scale and its application[J]. Control theory & applications, 2009, 26(10): 1069-1074.
[20] 刘景森, 袁蒙蒙, 左方. 面向全局搜索的自适应领导者樽海鞘群算法[J]. 控制与决策, 2021, 36(9): 2152-2160.
LIU J S, YUAN M M, ZUO F.Global search-oriented adaptive leader salp swarm algorithm[J]. Control and decision, 2021, 36(9): 2152-2160.
[21] TIZHOOSH H R.Opposition-based learning: a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). Vienna, Austria, 2006: 695-701.
[22] 程陈, 陈堂贤, 孙培胜, 等. 基于灰狼算法的光伏组件故障诊断模型优化[J]. 电源技术, 2022, 46(6): 684-687.
CHENG C, CHEN T X, SUN P S, et al.Optimization of fault diagnosis model for photovoltaic module based on gray wolf algorithm[J]. Chinese journal of power sources, 2022, 46(6): 684-687.
[23] 何根新, 周俊杰, 周家兴, 等. 基于自适应权重粒子群优化BP神经网络的光伏阵列故障诊断方法研究[J]. 电子设计工程, 2021, 29(9): 75-79.
HE G X, ZHOU J J, ZHOU J X, et al.Research on fault diagnosis method of photovoltaic array based on BP neural network with adaptive weight particle swarm optimization[J]. Electronic design engineering, 2021, 29(9): 75-79.
[24] 武文栋, 施保华, 郑传良, 等. 基于改进麻雀搜索算法优化RBF神经网络的光伏阵列故障诊断[J]. 智慧电力, 2023, 51(2): 77-83.
WU W D, SHI B H, ZHENG C L, et al.Fault diagnosis of photovoltaic array based on improved sparrow search algorithm optimized RBF neural network[J]. Smart power, 2023, 51(2): 77-83.
[25] HAFEZ A Z, YOUSEF A M, HARAG N M.Solar tracking systems: technologies and trackers drive types:areview[J]. Renewable and sustainable energy reviews, 2018, 91: 754-782.