Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach
Due to the nonlinear characteristics of photovoltaic (PV) cells, it remains as a challenge to generate stable maximum power from PV conversion systems. Under partial shading circumstances, PV characteristics present multiple local maximum power points. In this paper, a neural network improved dragon...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172734 |
| _version_ | 1826179035496972288 |
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| author | Song, Guangyu Liu, Xinghua Tian, Jiaqiang Xiao, Gaoxi Zhao, Tianyang Wang, Peng |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Song, Guangyu Liu, Xinghua Tian, Jiaqiang Xiao, Gaoxi Zhao, Tianyang Wang, Peng |
| author_sort | Song, Guangyu |
| collection | NTU |
| description | Due to the nonlinear characteristics of photovoltaic (PV) cells, it remains as a challenge to generate stable maximum power from PV conversion systems. Under partial shading circumstances, PV characteristics present multiple local maximum power points. In this paper, a neural network improved dragonfly algorithm (NNIDA) based approach is proposed to improve the performance of tracking the global maximum power point (GMPP). Specifically, the specific points on the I-V curves are sampled and analyzed such that the various ranges of irradiance and temperature are covered. Then, the iteration best solution of MPP (MPP\mathrm{IB}) can be quickly acquired by utilizing the fast approximation characteristics of NNIDA based approach. This process is independent of the configurations of PV modules and has no requirement for irradiance and temperature sensors. Further, the NNIDA based approach can locate the global best solution of MPP (MPP\mathrm{GB}) rapidly and precisely in a small interval. Particularly, an adaptive convergence factor is introduced into the NNIDA based approach to accelerate the convergence rate. Furthermore, the inertia weight is developed to improve the tracking accuracy. The comparative simulation and hardware-in-loop (HIL) examples validate the effectiveness of the proposed approach. |
| first_indexed | 2024-10-01T04:31:29Z |
| format | Journal Article |
| id | ntu-10356/172734 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:31:29Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1727342023-12-18T06:35:59Z Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach Song, Guangyu Liu, Xinghua Tian, Jiaqiang Xiao, Gaoxi Zhao, Tianyang Wang, Peng School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering PV Conversion Systems Neural Network Due to the nonlinear characteristics of photovoltaic (PV) cells, it remains as a challenge to generate stable maximum power from PV conversion systems. Under partial shading circumstances, PV characteristics present multiple local maximum power points. In this paper, a neural network improved dragonfly algorithm (NNIDA) based approach is proposed to improve the performance of tracking the global maximum power point (GMPP). Specifically, the specific points on the I-V curves are sampled and analyzed such that the various ranges of irradiance and temperature are covered. Then, the iteration best solution of MPP (MPP\mathrm{IB}) can be quickly acquired by utilizing the fast approximation characteristics of NNIDA based approach. This process is independent of the configurations of PV modules and has no requirement for irradiance and temperature sensors. Further, the NNIDA based approach can locate the global best solution of MPP (MPP\mathrm{GB}) rapidly and precisely in a small interval. Particularly, an adaptive convergence factor is introduced into the NNIDA based approach to accelerate the convergence rate. Furthermore, the inertia weight is developed to improve the tracking accuracy. The comparative simulation and hardware-in-loop (HIL) examples validate the effectiveness of the proposed approach. This work was supported in part by the National Natural Science Foundation of China under Grant U2003110, and in part by the High Level Talents Plan of Shaanxi Province for Young Professionals. 2023-12-18T06:35:59Z 2023-12-18T06:35:59Z 2023 Journal Article Song, G., Liu, X., Tian, J., Xiao, G., Zhao, T. & Wang, P. (2023). Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach. IEEE Transactions On Power Delivery, 38(5), 3179-3191. https://dx.doi.org/10.1109/TPWRD.2023.3271153 0885-8977 https://hdl.handle.net/10356/172734 10.1109/TPWRD.2023.3271153 2-s2.0-85159714163 5 38 3179 3191 en IEEE Transactions on Power Delivery © 2023 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering PV Conversion Systems Neural Network Song, Guangyu Liu, Xinghua Tian, Jiaqiang Xiao, Gaoxi Zhao, Tianyang Wang, Peng Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title | Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title_full | Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title_fullStr | Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title_full_unstemmed | Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title_short | Global maximum power point tracking for PV conversion systems under partial shadings: NNIDA based approach |
| title_sort | global maximum power point tracking for pv conversion systems under partial shadings nnida based approach |
| topic | Engineering::Electrical and electronic engineering PV Conversion Systems Neural Network |
| url | https://hdl.handle.net/10356/172734 |
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