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Photovoltaic MPPT Control Based on Improved DBO Algorithm
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Lingzhe KONG1, Menghan LIN2, Yongqin WEI1, Zhen LI1
Journal of Power Supply | 2026, 24(2) : 216 - 222
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Journal of Power Supply | 2026, 24(2): 216-222
• AI and Power Electronics •
Photovoltaic MPPT Control Based on Improved DBO Algorithm
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Lingzhe KONG1, Menghan LIN2, Yongqin WEI1, Zhen LI1
Affiliations
  • 1.School of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, China
  • 2.School of Intelligent Control, Wenzhou Technician Institute, Wenzhou 325003, China
Published: 2026-02-28 doi: 10.13234/j.issn.2095-2805.2026.2.216
Outline
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Under partial shading conditions, a multi-peak state will appear in the P-U curve of photovoltaic array. In this case, the conventional maximum power point tracking (MPPT) algorithm is prone to fall into local extremum, which cannot accurately track the maximum power point of a photovoltaic power generation system in time. Aimed at this problem, a MPPT control strategy based on an improved dung beetle optimizer (IDBO) algorithm is proposed. First, the targeted initialization of the dung beetle population is optimized, and the Levy flight strategy is introduced in the position update process. Through simulation verification and physical experiment verification on a MATLAB/Simulink platform, the IDBO algorithm can find the global maximum power point faster and more stably than the traditional algorithm under both the static and dynamic conditions.

Partial shading  /  photovoltaic power generation  /  maximum power point tracking (MPPT)  /  Levy flight  /  dung beetle optimizer
孔令哲, 林孟涵, 卫永琴, 李震. 基于改进蜣螂算法的光伏MPPT控制. 电源学报, 2026 , 24 (2) : 216 -222 . DOI: 10.13234/j.issn.2095-2805.2026.2.216
Lingzhe KONG, Menghan LIN, Yongqin WEI, Zhen LI. Photovoltaic MPPT Control Based on Improved DBO Algorithm[J]. Journal of Power Supply, 2026 , 24 (2) : 216 -222 . DOI: 10.13234/j.issn.2095-2805.2026.2.216
Appendix
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Year 2026 volume 24 Issue 2
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Article Info
doi: 10.13234/j.issn.2095-2805.2026.2.216
  • Receive Date：2023-04-07
  • Online Date：2026-10-29
  • Published：2026-02-28
Article Data
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History
  • Received：2023-04-07
  • Revised：2023-09-12
  • Accepted：2023-11-21
Affiliations
    1.School of Electrical and Automation Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    2.School of Intelligent Control, Wenzhou Technician Institute, Wenzhou 325003, 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
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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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