RESEARCH ON FAULT CHARACTERISTICS ANALYSIS AND DIAGNOSIS METHODS FOR DC-SIDE LEAKAGE IN PHOTOVOLTAIC GRID-CONNECTED INVERTERS

Li Changlong; Zhong Peijun; Ou Haonan; Su Sheng; Sun Jianjun; Su Huafeng

doi:10.19912/j.0254-0096.tynxb.2025-0402

PDF(1260 KB)

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 483-493. DOI: 10.19912/j.0254-0096.tynxb.2025-0402

RESEARCH ON FAULT CHARACTERISTICS ANALYSIS AND DIAGNOSIS METHODS FOR DC-SIDE LEAKAGE IN PHOTOVOLTAIC GRID-CONNECTED INVERTERS

Li Changlong^1,2, Zhong Peijun^1,2, Ou Haonan^1,2, Su Sheng^1,2, Sun Jianjun³, Su Huafeng⁴

Author information +

History +

Abstract

To meet the requirements of DC-side leakage current characteristic analysis and fault identification in photovoltaic grid-connected inverters, this paper first establishes an equivalent analysis model for DC-side leakage faults in non-isolated three-phase photovoltaic grid-connected inverters. Then, the formation mechanism and frequency-domain characteristics of leakage currents under DC-side faults are analyzed and the reasons for the formation of various frequency components are explained. After that, a multi-type fault identification method using the XGBoost algorithm is proposed based on the mechanistic and statistical characteristics of DC-side leakage currents.In the simulation cases, the proposed fault identification method based on mechanistic features and statistical features achieves an accuracy rate exceeding 99.5%, demonstrating significant superiority over traditional statistical feature-based approaches and conventional machine learning algorithms for fault detection. These results substantiate that incorporating mechanistic features can markedly enhance the identification accuracy of DC-side leakage fault types in grid-connected photovoltaic inverters.

Key words

grid-connected PV inverter / leakage current / fault diagnosis / mechanistic characteristics / statistical features / XGBoost algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Li Changlong, Zhong Peijun, Ou Haonan, Su Sheng, Sun Jianjun, Su Huafeng. RESEARCH ON FAULT CHARACTERISTICS ANALYSIS AND DIAGNOSIS METHODS FOR DC-SIDE LEAKAGE IN PHOTOVOLTAIC GRID-CONNECTED INVERTERS[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 483-493 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0402

References

[1] 王君, 余本东, 王矗垚, 等. 太阳能光伏光热建筑一体化(BIPV/T)研究新进展[J]. 太阳能学报, 2022, 43(6): 72-78.
Wang J, Yu B D, Wang C Y, et al.New advancements of building integrated photovoltaic/thermal system(BIPV/T)[J]. Acta Energiae Solaris Sinica, 2022, 43(6): 72-78.
[2] Singh T S D, Shimray B A, Meitei S N. Performance analysis of a rooftop grid-connected photovoltaic system in north-eastern India, Manipur[J]. Energies, 2025, 18(8): 1921.
[3] 肖华锋. 非隔离型光伏并网逆变器软开关技术[J]. 中国电机工程学报, 2019, 39(3): 812-821.
Xiao H F.Soft-switching techniques for transformerless photovoltaic grid-connected inverters[J]. Proceedings of the CSEE, 2019, 39(3): 812-821.
[4] 王立乔, 陈建医, 武晨. 一种能够抑制共模电流的单级单相Buck-Boost光伏逆变器[J]. 太阳能学报, 2022, 43(11): 41-49.
Wang L Q, Chen J Y, Wu C.A single-stage single-phase Buck-Boost PV inverter which can suppress leakage current[J]. Acta Energiae Solaris Sinica, 2022, 43(11): 41-49.
[5] 陆格野, 郑大勇, 林秋琼, 等. 基于漏电流的光伏并网系统共模阻抗特性分析与装备状态诊断[J]. 电工技术学报, 2025, 40(17): 5526-5538.
Lu G Y, Zheng D Y, Lin Q Q, et al.Common-mode impedance characteristics analysis and equipment condition diagnosis in photovoltaic grid-connected system based on leakage current[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5526-5538.
[6] DIN VDE0126-1-1—2006 Automatic disconnection device between a generator and the public low-voltage grid[S].
[7] 童树卫, 余志勇, 钱彬. 一种用于非隔离光伏并网逆变器漏电流检测的电路设计与实现[J]. 电子科学技术, 2015(2): 162-167.
Tong S W, Yu Z Y, Qian B.One kind of leakage current detection circuit design and realization for transformerless PV grid-connected inverter[J]. Electronic Science & Technology, 2015(2): 162-167.
[8] 康劲松, 张凤岗. 基于谐波提取的非隔离型并网光伏逆变器漏电流检测研究[J]. 中国电机工程学报, 2020, 40(7): 2113-2122.
Kang J S, Zhang F G.Research on leakage current detection of transformerless grid-connected photovoltaic inverter based on harmonic extraction[J]. Proceedings of the Chinese Society for Electrical Engineering, 2020, 40(7): 2113-2122.
[9] 张纯江, 柴秀慧, 何浩, 等. 基于独立分裂电容的非隔离型中性点箝位逆变器漏电流抑制[J]. 中国电机工程学报, 2020, 40(4): 1082-1094, 1401.
Zhang C J, Chai X H, He H, et al.Leakage current suppression of non-isolated neutral-point-clamped inverter based on independent split capacitor[J]. Proceedings of the Chinese Society for Electrical Engineering, 2020, 40(4): 1082-1094, 1401.
[10] 王宝诚, 郭小强, 杨勇, 等. 三电平四桥臂光伏逆变器漏电流抑制研究[J]. 中国电机工程学报, 2018, 38(14): 4194-4201.
Wang B C, Guo X Q, Yang Y, et al.Research on leakage current suppression for three-level four-leg PV inverter[J]. Proceedings of the Chinese Society for Electrical Engineering, 2018, 38(14): 4194-4201.
[11] 王俊澎, 王晓琳, 叶远茂. 基于开关电容的双接地五电平光伏逆变器[J]. 太阳能学报, 2023, 44(6): 234-241.
Wang J P, Wang X L, Ye Y M.Common-ground five-level photovoltaic inverter based on switched-capacitor[J]. Acta Energiae Solaris Sinica, 2023, 44(6): 234-241.
[12] Kerekes T, Teodorescu R, Liserre M.Common mode voltage in case of transformerless PV inverters connected to the grid[C]//2008 IEEE International Symposium on Industrial Electronics. Cambridge, UK, 2008: 2390-2395.
[13] 肖华锋, 谢少军, 陈文明, 等. 非隔离型光伏并网逆变器漏电流分析模型研究[J]. 中国电机工程学报, 2010, 30(18): 9-14.
Xiao H F, Xie S J, Chen W M, et al.Study on leakage current model for tansformerless photovoltaic grid-connected inverter[J]. Proceedings of the CSEE, 2010, 30(18): 9-14.
[14] 马海啸, 应雯. 一种用于漏电流抑制的改进型H7逆变器[J]. 太阳能学报, 2023, 44(2): 460-467.
Ma H X, Ying W.An improved H7 inverter for leakage current suppression[J]. Acta Energiae Solaris Sinica, 2023, 44(2): 460-467.
[15] 柳少良, 游小杰, 李艳, 等. 非隔离型单相光伏并网逆变器共模漏电流抑制方法研究[J]. 太阳能学报, 2014, 35(12): 2431-2437.
Liu S L, You X J, Li Y, et al.Solution of restraining leakage current for single-phase non-isolated PV grid-connected inverters[J]. Acta Energiae Solaris Sinica, 2014, 35(12): 2431-2437.
[16] 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): 1612-1622.
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): 1612-1622.
[17] 李泽文, 肖仁平, 杜昱东, 等. 集中式光伏并网输电线路的故障暂态分析与保护[J]. 电力系统自动化, 2019, 43(18): 120-130.
Li Z W, Xiao R P, Du Y D, et al.Fault transient analysis and protection for transmission lines with integration of centralized photovoltaic[J]. Automation of Electric Power Systems, 2019, 43(18): 120-130.
[18] 王乐, 陈雪, 张舒, 等. 光伏组件热斑效应研究[J]. 太阳能学报, 2023, 44(7): 155-161.
Wang L, Chen X, Zhang S, et al.Hot spot effect for photovoltaic modules[J]. Acta Energiae Solaris Sinica, 2023, 44(7): 155-161.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 邵阳, 武建文, 马速良, 等. 用于高压断路器机械故障诊断的AM-ReliefF特征选择下集成SVM方法[J]. 中国电机工程学报, 2021, 41(8): 2890-2900.
Shao Y, Wu J W, Ma S L, et al.Integrated SVM method with AM-ReliefF feature selection for mechanical fault diagnosis of high voltage circuit breakers[J]. Proceedings of the CSEE, 2021, 41(8): 2890-2900.
[24] 刘传洋, 吴一全. 基于红外图像的电力设备识别及发热故障诊断方法研究进展[J]. 中国电机工程学报, 2025, 45(6): 2171-2196.
Liu C Y, Wu Y Q.Research progress of power equipment identification and thermal fault diagnosis based on infrared images[J]. Proceedings of the CSEE, 2025, 45(6): 2171-2196.
[25] 翁楦乔, 文成林. 基于深度特征聚类和RNN的电网故障诊断[J]. 控制工程, 2022, 29(1): 175-181.
Weng X Q, Wen C L.Fault diagnosis of power grid based on deep feature clustering and recurrent neural network[J]. Control Engineering of China, 2022, 29(1): 175-181.
[26] 罗晨, 喻锟, 曾祥君, 等. 基于高频重构信号与Bayes-XGBoost的低压电弧故障辨识方法研究[J]. 电力系统保护与控制, 2023, 51(13): 91-101.
Luo C, Yu K, Zeng X J, et al.Low voltage arc fault identification method based on high frequency reconstructed signal and Bayes-XGBoost[J]. Power System Protection and Control, 2023, 51(13): 91-101.
[27] 张子洵, 魏业文, 张轲钦, 等. 基于ICOA-XGBoost的光伏阵列复合故障诊断研究[J]. 太阳能学报, 2025, 46(5): 251-259.
Zhang Z X, Wei Y W, Zhang K Q, et al.Research on composite fault diagnosis of photovoltaic arrays based on ICOA-XGBoost[J]. Acta Energiae Solaris Sinica, 2025, 46(5): 251-259.
[28] Wang L H, Wang T, Ma H, et al.Time-series SAR monitoring of rice in multiple cropping modes combining statistical and phenological characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 4411811.
[29] Morales-Caporal R, Perez-Cuapio J F, Martinez-Hernandez H P. Design and hardware implementation of a H-bridge sub-module for single-phase 5-level cascaded voltage source inverters[C]//IECON 2021: 47th Annual Conference of the IEEE Industrial Electronics Society. Toronto, ON, Canada, 2021: 1-6.
[30] He C M, Li X G, Xia Y, et al.Addressing the overfitting in partial domain adaptation with self-training and contrastive learning[J]. IEEE Transactions on Circuits and Systems For Video Technology, 2024, 34(3): 1532-1545.