Research on Photovoltaic MPPT Based on Improved Butterfly Algorithm

To solve slow response speed and poor stability of traditional MPPT algorithms under rapidly changing environmental conditions, a MPPT control strategy combining improved butterfly algorithm and disturbance observation method is proposed. Firstly, the butterfly position in the population is initialized by Tent map, increasing the search space; Secondly, Gaussian mutation strategy is introduced to make the algorithm break out of local extremes; Then, a dynamic probability switching mechanism is adopted to balance the weight relationship between global and local search, improving algorithm optimization efficiency; Finally, in the later stage, disturbance observation method is used to track the maximum power and reduce power oscillation. Simulation examples show that the proposed algorithm can find the maximum power point quickly and accurately under both static and dynamic shading conditions, and the power output is more stable.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/icpre62586.2024.10768583
Primary Topic
Power Systems and Renewable Energy
Type
article
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article

Research on Photovoltaic MPPT Based on Improved Butterfly Algorithm

Zun Ma, Liangping Tu, Binjiang Hu, Yihua Zhu et al.
Power Systems and Renewable Energy
article

Research on Photovoltaic MPPT Based on Improved Butterfly Algorithm

Zun Ma, Liangping Tu, Binjiang Hu, Yihua Zhu, Chao Xing, Xian Meng
article en

Abstract

To solve slow response speed and poor stability of traditional MPPT algorithms under rapidly changing environmental conditions, a MPPT control strategy combining improved butterfly algorithm and disturbance observation method is proposed. Firstly, the butterfly position in the population is initialized by Tent map, increasing the search space; Secondly, Gaussian mutation strategy is introduced to make the algorithm break out of local extremes; Then, a dynamic probability switching mechanism is adopted to balance the weight relationship between global and local search, improving algorithm optimization efficiency; Finally, in the later stage, disturbance observation method is used to track the maximum power and reduce power oscillation. Simulation examples show that the proposed algorithm can find the maximum power point quickly and accurately under both static and dynamic shading conditions, and the power output is more stable.

Electric Power Research Institute (US), China Southern Power Grid (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 72%
Power Systems and Renewable Energy
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