Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions

Yang, Bo, Wu, Shaocong, Huang, Jianxiang, Guo, Zhengxun, Wang, Jiarong, Zhang, Zijian, Xie, Rui, Shu, Hongchun and Jiang, Lin ORCID: 0000-0001-6531-2791 (2023) Salp swarm optimization algorithm based MPPT design for PV-TEG hybrid system under partial shading conditions. Energy Conversion and Management, 292. p. 117410.

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Official URL: http://dx.doi.org/10.1016/j.enconman.2023.117410

Abstract

This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta-heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system's optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by ΔVavg and ΔVmax).

Item Type:	Article
Uncontrolled Keywords:	Rare Diseases, 7 Affordable and Clean Energy
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	12 Oct 2023 07:37
Last Modified:	15 Mar 2024 04:51
DOI:	10.1016/j.enconman.2023.117410
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3173611