Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

Cherukuri, Santhan Kumar; Rayapudi, Srinivasa Rao

doi:10.14710/ijred.6.3.203-212

Cited by 55 publications

(29 citation statements)

References 8 publications

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“…Finally, the proposed MPP search will finish when only a few of the best individuals converge in a specified range. As a result, the convergence time of SDRA is less than other methods, such as improved PSO [15], [25] ACO [12], enhanced GWO [17], bat algorithm (BA) [19], and hybrid method [26].…”

Section: Pdmentioning

confidence: 99%

“…Although the improved PSO can track time-variant global MPP, it still has undesirable steady-state oscillations around this global MPP. The GWA method and the shuffled frog leap algorithm (SFLA) method can efficiently find the global MPP with greater accuracy and less computation time than other methods, as stated in [17] and [18]. The BA [19] and CS [20] have been issued with better demonstrations by numerical simulation and experimental results.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Algorithm-based MPPT Strategy for PV Power Systems under Partial Shading Conditions

Chiu

Ngo

2022

ELEKTRON ELEKTROTECH

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This paper proposes a new simple method based on the simulation of a short-distance running race in athletics to track the maximum power point (MPP) for photovoltaic (PV) power systems, which can improve the tracking speed and search accuracy. In detail, the DC-DC boost converter is utilized to transfer the power of PV panels to a load and follows the MPP all the time, regardless of the environmental temperature and variant solar irradiance. In the MPP search method based on the proposed short-distance running algorithm (SDRA), an appropriate duty ratio value will be found so that the load receives the highest electrical power from the PV energy system. As a result, the SDRA method has excellent MPP tracking ability with high convergence speed and no oscillation. The efficiency of the proposed method is verified by simulation and experiments. The proposed SDRA method overcomes the obstruction of local traps to achieve global MPP. The results have shown that the SDRA method has advantages in terms of better convergence speed and performance than the particle swarm optimization (PSO) and grey wolf algorithm (GWA) methods when they are operated under the same conditions.

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Section: Pdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm-based MPPT Strategy for PV Power Systems under Partial Shading Conditions

Chiu

Ngo

2022

ELEKTRON ELEKTROTECH

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“…⃗ and ⃗ are the coefficients, to equilibrium the exploration and the exploitation [7]. Value of ⃗ are updated from 2 to 0 as given (5), ⃗ and ⃗ , can be randomly selected in the range [0-1].…”

Section: Encircling Preymentioning

confidence: 99%

Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization

Köybaşi

Yazici

2020

Sakarya University Journal of Science

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In this study, Grey Wolf Optimization (GWO), which is a new method with swarm intelligence is compared with another metaheuristic optimization method, Particle Swarm Optimization (PSO), using optimization benchmark functions. Simulation studies on test functions are presented as a table by obtaining mean, standard deviation, best and worst values. In addition, the effects of population and iteration number change on the GWO algorithm are presented in separate tables. The GWO algorithm has establish a good balance between exploration and exploitation. Simulation studies have shown that GWO has better convergence performance and optimization accuracy.

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“…They have a very strong dominant leadership and hierarchical such as military system [57], which can be classified into four levels (leadership pyramids); leaders wolves called alpha (α), subleaders called beta ( ), delta (δ) and the lowest wolves called omega (ω) wolves as shown in Fig. 7 [33,57], where the dominance of wolves increases from top to bottom [58]. The behaviour of these Grey Wolves is inspired in the optimisation field such as PV energy system.…”

Section: Gwo-based Gmpptmentioning

confidence: 99%

Grade point average assessment for metaheuristic GMPP techniques of partial shaded PV systems

Eltamaly

Farh

Al-Saud

2019

IET Renewable Power Generation

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Photovoltaic (PV) maximum power point tracker (MPPT) is compulsory in PV systems to improve its output power and efficiency. Conventional techniques can track the maximum power under uniform irradiances efficiently and accurately. Nevertheless, in case of partial shading conditions where multiple peaks are generated, these conventional techniques may stick at any local peak of the P-V curve of the PV energy systems. Metaheuristic techniques have been applied to PV energy systems to overcome this limitation, where most of these techniques can catch the global MPP (GMPP) easily and efficiently. In case of dynamic change of partial shading, most of these techniques need reinitialisation to disperse the search agents to look again for a new position and value of GMPP. This study introduces a brief description, assessment and evaluation for these techniques. In addition, it proposes a novel assessment criterion based on grade point average for evaluating and ranking the 20 famous metaheuristic and hybrid GMPP techniques. This evaluation methodology can help researchers, designer and decision maker to choose the best option for MPPT of dynamic change of partially shading PV energy systems.

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Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition

Cited by 55 publications

References 8 publications

A Novel Algorithm-based MPPT Strategy for PV Power Systems under Partial Shading Conditions

A Novel Algorithm-based MPPT Strategy for PV Power Systems under Partial Shading Conditions

Solution of Test Problems with Grey Wolf Optimization Algorithm and Comparison with Particle Swarm Optimization

Grade point average assessment for metaheuristic GMPP techniques of partial shaded PV systems

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