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Fault Diagnosis Method for Photovoltaic Arrays Based on IDBO-LightGBM
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Yajun WU, Lu WANG, Jinjiang ZHANG  Member, CPSS
Journal of Power Supply | 2026, 24(4) : 122 - 132
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Journal of Power Supply | 2026, 24(4): 122-132
• Renewable Energy Generation and Energy Storage •
Fault Diagnosis Method for Photovoltaic Arrays Based on IDBO-LightGBM
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Yajun WU, Lu WANG, Jinjiang ZHANG  Member, CPSS
Affiliations
  • School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Published: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.122
Outline
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To improve the accuracy of fault diagnosis for photovoltaic arrays, a photovoltaic fault diagnosis method based on an improved dung beetle optimization (IDBO) algorithm which is used to optimize a light gradient boosting machine (LightGBM) is proposed. The IDBO algorithm is used to optimize specific hyperparameters in LightGBM, and a fault diagnosis model for photovoltaic arrays based on IDBO-LightGBM is established. To effectively distinguish various faults, the characteristic points extracted from the current-voltage curves are utilized. To validate the effectiveness of the proposed method, simulation analysis is conducted on the model. A comparison is performed among the proposed method, LightGBM algorithm, DBO-LightGBM algorithm, other decision tree algorithms and support vector machine algorithm, and results verify the stability and accuracy of the novel method.

Photovoltaic array  /  fault diagnosis  /  improved dung beetle optimization (IDBO) algorithm  /  light gradient boosting machine (LightGBM)
吴亚钧, 王璐, 张金江中国电源学会会员. 基于IDBO-LightGBM的光伏阵列故障诊断方法. 电源学报, 2026 , 24 (4) : 122 -132 . DOI: 10.13234/j.issn.2095-2805.2026.4.122
Yajun WU, Lu WANG, Jinjiang ZHANG. Fault Diagnosis Method for Photovoltaic Arrays Based on IDBO-LightGBM[J]. Journal of Power Supply, 2026 , 24 (4) : 122 -132 . DOI: 10.13234/j.issn.2095-2805.2026.4.122
  • “Pioneer” and “Leading Goose” R&D Program of Zhejiang(2025C01149)
Year 2026 volume 24 Issue 4
38
3
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.4.122
  • Receive Date:2023-11-02
  • Online Date:2026-10-29
  • Published:2026-04-30
Article Data
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History
  • Received:2023-11-02
  • Revised:2023-12-18
  • Accepted:2023-12-22
Funding
“Pioneer” and “Leading Goose” R&D Program of Zhejiang(2025C01149)
Affiliations
    School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
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Genus
种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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