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...

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Main Authors: Song, Guangyu, Liu, Xinghua, Tian, Jiaqiang, Xiao, Gaoxi, Zhao, Tianyang, Wang, Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172734
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
PV Conversion Systems
Neural Network
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
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.
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
format Article
author Song, Guangyu
Liu, Xinghua
Tian, Jiaqiang
Xiao, Gaoxi
Zhao, Tianyang
Wang, Peng
author_sort Song, Guangyu
title 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_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_sort global maximum power point tracking for pv conversion systems under partial shadings: nnida based approach
publishDate 2023
url https://hdl.handle.net/10356/172734
_version_ 1787136533755592704