Gravitational Search Algorithm (GSA) based Maximum Power Point Tracking in a Solar PV based Generation System

Pervez, Imran; Sarwar, Adil; Tayyab, Mohammad; Sarfraz, Mohammad

doi:10.1109/i-pact44901.2019.8960130

Cited by 11 publications

(2 citation statements)

References 15 publications

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“…Therefore, many countries invest heavily in the solar PV energy market, keeping in view the future potential. Furthermore, different countries incentives and consumer‐friendly policies have motivated various industries to produce their own electrical power, resulting in rapid growth in the generation of electrical power through solar PV generation 1‐5 . The concept of the smart grid has also accelerated the widespread use of solar PV generation.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, different countries incentives and consumer-friendly policies have motivated various industries to produce their own electrical power, resulting in rapid growth in the generation of electrical power through solar PV generation. [1][2][3][4][5] The concept of the smart grid has also accelerated the widespread use of solar PV generation. In spite of the advantages mentioned above, there are several challenges in solar PV power generation.…”

Section: Introductionmentioning

confidence: 99%

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Maximum power point tracking in a solar PV system: Current trends towards nature‐inspired optimization techniques

Tayyab

Sarwar

Parvez

et al. 2021

Int Trans Electr Energ Syst

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This paper discusses and analyses the work done in developing a Natureinspired optimization algorithm in a solar PV system for tracking the global maximum power point (GMPP) in partial shaded condition (PSC). Partial shading is a situation where PV panels connected in series do not receive the same solar radiation, thus exhibiting different I-V characteristics for the individual panel. Under PSC, solar PV system output exhibits multiple local maximum power point (MPP) and a unique global MPP on the Power-Voltage curve. Conventional maximum power point tracking (MPPT) techniques such as Perturb and Observe (P&O), incremental conductance (IC), and Hill Climbing (HC) method can only track MPP under uniform insolation conditions. It may fail to track GMPP in case of PSC and get trapped in local MPP, thereby causing loss of power that could have been tapped if the operation would have been at GMPP. To overcome the problem posed by PSC, researchers are applying the nature-inspired optimization algorithm for tracking GMPP under partial shading conditions with fairly good results. Nature-inspired optimization algorithm offers the advantage of solving multivariable nonlinear objective functions with constraints by exploiting the search space and can converge quickly to GMPP. Some of the most recent and famous nature-inspired algorithms have been discussed and compared here. Simulation and hardware results of some of them are also taken in order to compare them effectively. Simulation results show that the Most Valuable Player Algorithm (MVPA) takes the least time to track the MPP in all the cases, and the MPP tracked is also comparable List of Symbols and Abbreviations: X k , Position corresponding to the kth iteration; U k , Voltage corresponding to the kth iteration; P pv , Output power of the solar PV; k, Iteration number; d, Duty cycle; v k , Velocity of the particle corresponding to the kth iteration; ω, Inertia weight; c 1 , cognitive coefficient; c 2 , Social coefficient; r 1 and r 2 , Uniformly distributed random variables between [0,1]; p best,k , Best position of the kth particle; g best , Best position amongst all particles; X p , Prey position; A, C, and D, Coefficient vectors; HIL, Hardware-in-the-loop.

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Section: Introductionmentioning

confidence: 99%