Article citationsMore>>
Yang, B., Zhu, T., Wang, J., Shu, H., Yu, T., Zhang, X., Yao, W. and Sun, L. (2020) Comprehensive Overview of Maximum Power Point Tracking Algorithms of PV Systems under Partial Shading Condition. Journal of Cleaner Production, 268, Article ID: 121983.
https://doi.org/10.1016/j.jclepro.2020.121983
has been cited by the following article:
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TITLE:
Optimal Control of Hybrid Photovoltaic-Thermometric Generator System Using GEPSO
AUTHORS:
Maryam Ejaz
KEYWORDS:
Thermoelectric Generator (TEG), PV System, Maximum Power Point Tracking (MPPT), Photovoltaic (PV), Generalized Particle Swarm Optimization (GEPSO), Energy Harvesting
JOURNAL NAME:
Journal of Power and Energy Engineering,
Vol.10 No.3,
March
8,
2022
ABSTRACT: Recently
the concern about energy consumption across the globe has become more severe
due to global warming. One essential way to address this problem is to maximize
the efficiency of existing renewable energy resources and effectively eliminate
their power losses. The previous studies on energy harvesting of photovoltaic
(PV) modules try to cope with this problem using gradient-based control
techniques and pay little attention to the significant loss of solar energy in
the form of waste heat. To reconcile these waste-heat problems, this paper
investigates hybrid photovoltaic-thermoelectric generation (PV-TEG) systems. We
implement the generalized particle swarm optimization (GEPSO) technique to
maximize the power of PV systems under dynamic conditions by utilizing the
waste heat to produce electricity through embedding the thermoelectric
generator (TEG) with the PV module. The removal of waste heat increases the
efficiency of PV systems and also adds significant electrical power. As a
control method, the proposed GEPSO can maximize the output power. Simulations
confirm that GEPSO outperforms some state-of-the-art methods, e.g., the perturb
and observe (PO), cuckoo search (CS), incremental conductance (INC), and
particle swarm optimization (PSO), in terms of accuracy and tracking speed.