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Single-Sensor Global MPPT for PV System Interconnected with DC Link Using Recent Red-Tailed Hawk Algorithm.
- Source :
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Energies (19961073) . Jul2024, Vol. 17 Issue 14, p3391. 17p. - Publication Year :
- 2024
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Abstract
- The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19961073
- Volume :
- 17
- Issue :
- 14
- Database :
- Academic Search Index
- Journal :
- Energies (19961073)
- Publication Type :
- Academic Journal
- Accession number :
- 178696355
- Full Text :
- https://doi.org/10.3390/en17143391