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Zhang, X., Li, S., He, T., Yang, B., Yu, T., Li, H., Jiang, L. and Sun, L. (2019) Memetic Reinforcement Learning Based Maximum Power Point Tracking Design for PV Systems under Partial Shading Condition. Energy, 174, 1079-1090.
https://doi.org/10.1016/j.energy.2019.03.053

has been cited by the following article:

• 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.