RESEARCH ON COMPOSITE FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAYS BASED ON ICOA-XGBoost

Zhang Zixun; Wei Yewen; Zhang Keqin; Fang Hao; Wu Xianyong

doi:10.19912/j.0254-0096.tynxb.2024-0019

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 251-259. DOI: 10.19912/j.0254-0096.tynxb.2024-0019

RESEARCH ON COMPOSITE FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAYS BASED ON ICOA-XGBoost

Zhang Zixun¹, Wei Yewen^1,2, Zhang Keqin¹, Fang Hao¹, Wu Xianyong¹

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Abstract

In order to enhance the accuracy of composite fault diagnosis in photovoltaic （PV） arrays, this study proposes a method utilizing an improved coati optimization algorithm （ICOA） to optimize eXtreme Gradient Boosting （XGBoost）. Initially, a 9-dimensional fault feature vector is constructed as the input variable of the model by analyzing the output characteristics of PV arrays under different fault states. Then, the ICOA algorithm, which integrates the improved Circle chaotic mapping, Levy flight and t-distributed stochastic perturbation, is compared with the sparrow search algorithm （SSA）, the whale optimization algorithm （WOA）, and the coati optimization algorithm （COA）, demonstrateing superiority in optimization ability, stability and convergence speed. Subsequently, the improved ICOA algorithm is used to optimize the XGBoost model, which effectively solves the problem of setting the initialization parameters of the model. The experimental evidence indicates that the ICOA-XGBoost model, which integrates 9-dimensional fault feature vectors, achieves a fault diagnosis accuracy of 97.23%, outperforming other models and confirming the proposed method’s feasibility and effectiveness.

Key words

photovoltaic arrays / fault diagnosis / improved coati optimization algorithm（ICOA） / eXtreme Gradient Boosting（XGBoost）

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Zhang Zixun, Wei Yewen, Zhang Keqin, Fang Hao, Wu Xianyong. RESEARCH ON COMPOSITE FAULT DIAGNOSIS OF PHOTOVOLTAIC ARRAYS BASED ON ICOA-XGBoost[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 251-259 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0019

References

[1] 肖佳, 梅琦, 黄晓琪, 等. “双碳” 目标下我国光伏发电技术现状与发展趋势[J]. 天然气技术与经济, 2022, 16(5): 64-69.
XIAO J, MEI Q, HUANG X Q, et al.Status quo and development trend of photovoltaic power-generating technology under the dual-carbon goal[J]. Natural gas technology and economy, 2022, 16(5): 64-69.
[2] 李智华, 马浩强, 吴春华, 等. 基于三参数的光伏组件老化程度诊断[J]. 中国电机工程学报, 2022, 42(9): 3327-3338.
LI Z H, MA H Q, WU C H, et al.Diagnosing the aging degree of photovoltaic modules based on three parameters[J]. Proceedings of the CSEE, 2022, 42(9): 3327-3338.
[3] 张希康, 李泽滔. 光伏阵列故障诊断算法研究综述[J]. 智能计算机与应用, 2022, 12(2): 143-147.
ZHANG X K, LI Z T.A survey on photovoltaic array fault diagnosis algorithms[J]. Intelligent computer and applications, 2022, 12(2): 143-147.
[4] KUMAR B P, PILLAI D S, RAJASEKAR N, et al.Identification and localization of array faults with optimized placement of voltage sensors in a PV system[J]. IEEE transactions on industrial electronics, 2021, 68(7): 5921-5931.
[5] 宋文海, 李田泽, 乔家振, 等. TCT结构光伏阵列故障检测方法研究[J]. 电源技术, 2019, 43(7): 1164-1167.
SONG W H, LI T Z, QIAO J Z, et al.Research on fault detection method of TCT structured photovoltaic array[J]. Chinese journal of power sources, 2019, 43(7): 1164-1167.
[6] 曾祥超, 张鹤仙, 王水威, 等. 双面光伏组件I-V测试方法研究[J]. 太阳能学报, 2021, 42(3): 370-374.
ZENG X C, ZHANG H X, WANG S W, et al.Research on testing methods of I-V characteristics of bifacial pv modules[J]. Acta energiae solaris sinica, 2021, 42(3): 370-374.
[7] 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.
[8] HE Y Z, DU B L, HUANG S D.Noncontact electromagnetic induction excited infrared thermography for photovoltaic cells and modules inspection[J]. IEEE transactions on industrial informatics, 2018, 14(12): 5585-5593.
[9] 赵靖英, 吴晶晶, 张雪辉, 等. 基于萤火虫扰动麻雀搜索算法-极限学习机的光伏阵列故障诊断方法研究[J]. 电网技术, 2023, 47(4): 1612-1625.
ZHAO J Y, WU J J, ZHANG X H, et al.Fault diagnosis of photovoltaic arrays based on sparrow search algorithm with firefly perturbation-extreme learning machine[J]. Power system technology, 2023, 47(4): 1612-1625.
[10] 李斌, 郭自强, 高鹏. 改进北方苍鹰算法在光伏阵列中应用研究[J]. 电子测量与仪器学报, 2023, 37(7): 131-139.
LI B, GUO Z Q, GAO P.Application of improved northern goshawk optimization algorithm in photovoltaic array[J]. Journal of electronic measurement and instrumentation, 2023, 37(7): 131-139.
[11] 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.
[12] MOMENI H, SADOOGI N, FARROKHIFAR M, et al.Fault diagnosis in photovoltaic arrays using GBSSL method and proposing a fault correction system[J]. IEEE transactions on industrial informatics, 2020, 16(8): 5300-5308.
[13] 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.
[14] 王元章, 李智华, 吴春华. 一种四参数的光伏组件在线故障诊断方法[J]. 中国电机工程学报, 2014, 34(13): 2078-2087.
WANG Y Z, LI Z H, WU C H.A survey of online fault diagnosis for photovoltaic modules based on four parameters[J]. Proceedings of the CSEE, 2014, 34(13): 2078-2087.
[15] 李红岩, 王磊, 安平娟, 等. 基于改进黏菌算法的局部遮阴下光伏MPPT研究[J]. 太阳能学报, 2023, 44(10): 129-134.
LI H Y, WANG L, AN P J, et al.Study on photovoltaic MPPT under local shade based on improved slime mold algorithm[J]. Acta energiae solaris sinica, 2023, 44(10): 129-134.
[16] DHALIWAL S S, NAHID A A, ABBAS R.Effective intrusion detection system using XGBoost[J]. Information, 2018, 9(7): 149.
[17] 陈振宇, 刘金波, 李晨, 等. 基于LSTM与XGBoost组合模型的超短期电力负荷预测[J]. 电网技术, 2020, 44(2): 614-620.
CHEN Z Y, LIU J B, LI C, et al.Ultra short-term power load forecasting based on combined LSTM-XGBoost model[J]. Power system technology, 2020, 44(2): 614-620.
[18] 苏连成, 朱娇娇, 郭高鑫, 等. 基于XGBoost和Wasserstein距离的风电机组塔架振动监测研究[J]. 太阳能学报, 2023, 44(1): 306-312.
SU L C, ZHU J J, GUO G X, et al.Research on wind turbine tower vibration monitoring based on XGBoost and Wasserstein distance[J]. Acta energiae solaris sinica, 2023, 44(1): 306-312.
[19] BAŞ E, YILDIZDAN G.Enhanced coati optimization algorithm for big data optimization problem[J]. Neural processing letters, 2023, 55(8): 10131-10199.
[20] 宋立钦, 陈文杰, 陈伟海, 等. 基于混合策略的麻雀搜索算法改进及应用[J]. 北京航空航天大学学报, 2023, 49(8): 2187-2199.
SONG L Q, CHEN W J, CHEN W H, et al.Improvement and application of hybrid strategy-based sparrow search algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(8): 2187-2199.
[21] 赵莹莹, 何怡刚, 杜博伦, 等. 基于LSSA优化DBN的双有源桥变换器开路故障诊断[J]. 电子测量与仪器学报, 2022, 36(4): 56-64.
ZHAO Y Y, HE Y G, DU B L, et al.Open circuit fault diagnosis of dual active bridgeconverterbased on LSSA optimized DBN[J]. Journal of electronic measurement and instrumentation, 2022, 36(4): 56-64.
[22] 熊磊, 苗雨润, 范新舟, 等. 一种利用改进麻雀搜索算法的中央空调系统节能控制方法[J]. 上海交通大学学报, 2023, 57(4): 495-504.
XIONG L, MIAO Y R, FAN X Z, et al.Energy-saving control of central air-conditioning system based on an improved-SSA[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 495-504.
[23] 李争, 罗晓瑞, 张杰, 等. 基于改进麻雀搜索算法的光伏功率短期预测[J]. 太阳能学报, 2023, 44(6): 284-289.
LI Z, LUO X R, ZHANG J, et al.Short term prediction of photovoltaic power based on improved sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(6): 284-289.
[24] 付华, 刘昊. 多策略融合的改进麻雀搜索算法及其应用[J]. 控制与决策, 2022, 37(1): 87-96.
FU H, LIU H.Improved sparrow search algorithm with multi-strategy integration and its application[J]. Control and decision, 2022, 37(1): 87-96.
[25] 王元章, 吴春华, 周笛青, 等. 基于BP神经网络的光伏阵列故障诊断研究[J]. 电力系统保护与控制, 2013, 41(16): 108-114.
WANG Y Z, WU C H, ZHOU D Q, et al.A survey of fault diagnosis for PV array based on BP neural network[J]. Power system protection and control, 2013, 41(16): 108-114.