Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions

Kishore, D. J. K.; Mohamed, Mohd Rusllim; Sudhakar, K.; Peddakapu, K.

doi:10.1016/j.energy.2022.126366

Cited by 38 publications

(15 citation statements)

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“…Te authors in [7] analysed the performance at diferent irradiation and PSC. Refrences [12,14,15,18,20,33] compared RMSE and, the authors in [20] in terms of MAE. In this paper, three cases, STC, stepchanging irradiation, and PSC, are taken to validate the performance of JFO in terms of maximum power tracking, total execution time, the minimum number of iterations to achieve the GMPP with high tracking efciency, and RMSE for the same set of population and number of iterations.…”

Section: African Vultures Optimization Algorithmmentioning

confidence: 99%

“…A few of the well-known optimization techniques are artifcial immune system (AIS) [8], ant colony optimization (ACO) [5,8,[12][13][14], moth-fame optimization (MFO) [12,15], whale optimization algorithm (WOA) [12], butterfy optimization (BFO) algorithm [12], shufed frog leaping algorithms (SFLA) [12], slap swarm algorithm [12], frefy algorithm (FA) [12,16,17], artifcial bee colony (ABC) [5,8,9,12,13,[16][17][18][19][20], bat-search algorithm (BA) [12,17], grey wolf optimization (GWO) [5,12,13,15,17,21,22], genetic algorithm (GA) [8, 12-14, 21, 23], fower pollination optimization (FPO) [12,16,24], JAYA [16], DE [8,16], cuckoo search (CS) [5,6,12,13,15,16,…”

Section: Introductionmentioning

confidence: 99%

“…Te literature research on fnding popular optimization approaches is shown in Table 1. According to the authors in [20], the PSO gives optimum efciency even under PSC, but it quickly falls into local optima, and as a result, determining MPP takes a long time. ABC has a strong exploration ability to follow MPP, though these approaches are highly prone to exploitation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Yadav,

Singh,

Giri

et al. 2024

International Journal of Intelligent Systems

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This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R2, and VIF. The R2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO.

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Section: African Vultures Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Yadav,

Singh,

Giri

et al. 2024

International Journal of Intelligent Systems

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“…Regulations that support photovoltaic (PV), which harnesses the energy of the sun to create light and power, as well as a recent, dramatic drop in price, are causing its use to expand quickly. Due to its environmental friendly nature and ability to be put in both remote and residential settings, PV systems [5][6][7] have recently grown in popularity. The depletion of traditional sources of electricity is causing a daily increase in the need for renewable energy sources.…”

Section: Introductionmentioning

confidence: 99%

“…The fact that its local mode is still present causes this to have problems. The Artificial Bee Colony (ABC) [6,18] algorithm is another one that relies on optimization. When handling subsequent events, ABC performance has a slow convergence rate, however iteration numbering and population size increases can improve this.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent and Hybrid PDOA+MO based MPPT Controlling Mechanism for Grid-PV Systems

Marlin

Jebaseelan

2023

Preprint

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Due to its effectiveness and affordability, solar Photovoltaic (PV) systems are now being employed more and more in many application systems. The biggest challenge is still getting the most energy out from PV panels in a variety of climatic situations. In order to achieve the maximum energy production, several optimization-based MPPT controlling techniques have been developed in traditional works. However, it has significant issues with low convergence, computational complexity, a long time needed to find the best solution, and inefficiency. As a result, the goal of this research is to implement a novel and hybrid optimization technique, named as, Prairie Dog Optimization Algorithm (PDOA) + Mongoose Optimization (MO) for extracting the highest possible energy from PV panels. The original contribution of this work is to incorporate the functions of two different and recently developed optimization techniques for MPPT controlling. For this purpose, the PDOA and MO algorithms are taken into account, and the hybridized PDOA+MO provides the benefits of fast convergence, increased tracking efficiency, and reduced tracking time. The high gain Luo DC-DC converter is also used to increase PV's output power and voltage while minimising switching and conduction losses. Consequently, the voltage source inverter is used to suppress the level of harmonics for providing the quality improved AC output to the grid system. The suggested PDOA+MO algorithm's effectiveness and power tracking performance are validated through a thorough simulation analysis using a variety of parameters.

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Renewable‐Energy‐Powered EV Charging Station for Microgrid PSO‐Based Controller for PV‐Powered EV Charging Station

Punitha,

Aruna,

Kanthammal

2024

Microgrids for Commercial Systems

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Swarm intelligence-based MPPT design for PV systems under diverse partial shading conditions

Cited by 38 publications

References 50 publications

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

Comparison of Bioinspired Techniques for Tracking Maximum Power under Variable Environmental Conditions

An Intelligent and Hybrid PDOA+MO based MPPT Controlling Mechanism for Grid-PV Systems

Renewable‐Energy‐Powered EV Charging Station for Microgrid PSO‐Based Controller for PV‐Powered EV Charging Station

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