Dragonfly Algorithm Based Global Maximum Power Point Tracker for Photovoltaic Systems

Raman, G. Venkat; Raman, Gurupraanesh; Chakkarapani, M.; Ilango, G. Saravana

doi:10.1007/978-3-319-41000-5_21

Cited by 22 publications

(3 citation statements)

References 9 publications

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“…In [41,42], DA has been applied to the automatic generation control problem to optimize the gains of the controller. Furthermore, DA has been used in power transmission systems [43] for optimizing the size and cost of static var compensator, in PV panel [44] to maximize the tracking of solar power, in photovoltaic system [45] to track the global maximum power point (GMPP) and in Photovoltaic-biomass system [46] to find the optimal size and cost of grid-integrated renewable energy resources. DA has also been applied to the optimal reactive power dispatch problem for minimization of the power loss in transmission lines in [47] and to the load frequency control of electric power generating system to tune the gains and fractional order parameters of the controller in [48].…”

Section: Electrical Engineeringmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

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Section: Electrical Engineeringmentioning

confidence: 99%

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Emambocus

Jasser

Amphawan

2021

Sensors

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“…In (Teshome et al, 2016), the firefly algorithm (FA) was developed to design the MPPT for considering PSCs. In (Raman et al, 2016), the dragonfly algorithm (DA) was applied for tracking the MPP for a PV system in PSC. In (Fathy and Rezk, 2016), the mine blast algorithm (MBA) and teaching-learning-based optimization (TLBO) were applied to achieve the GMPP for PV considering PSC.…”

Section: Introductionmentioning

confidence: 99%

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

Alanazi

Memon

et al. 2022

Front. Energy Res.

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In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using a hill climbing (HC)–artificial electric field algorithm (AEFA) considering a DC/DC buck converter. The AEFA is inspired by Coulomb’s law of electrostatic force and has a high speed and optimization accuracy. Because the traditional HC method cannot perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The critical advantage of the HC-AEFA is that it is desirable performing local and global searches. The proposed hybrid method is implemented to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. Its capability is evaluated and compared with well-known particle swarm optimization (PSO), considering standards, PSCs, and radiation changes conditions. The tracking efficiency for the most challenging shading pattern (third pattern) using the HC-AEFA, HC, AEFA and PSO is obtained at 99.93, 90.35, 98.85, 99.80%, respectively. The analysis of the population-based optimization process for different algorithms proved the HC-AEFA faster convergence at lower iterations than the other methods. So, the superiority of the proposed HC-AEFA subjected to different patterns is confirmed with higher tracking efficiency and global power peak, fewer fluctuations, higher convergence speed, and higher dynamic and Static-efficiency compared to the other methods.

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“…Another reason for a non-diverse population is that the parameter choice can influence the algorithm's performance [26]. To balance AFPA's exploration and exploitation process, the other proposed algorithm DFPA utilizes the navigational behavioral characteristics of the Dragonfly algorithm that mimics the swarming behaviors of dragonflies, which are similar also to the exploration and exploitation phases of optimization [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

Sapul¹,

Setthawong²,

Setthawong³

2020

IJEECS

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University course timetable problem (UCTP) is one of the problems on which many researches have been conducted over the years because of its importance in academic institutions. A nature-inspired metaheuristic optimization algorithm, Flower Pollination Algorithm (FPA) has been adapted, so-called Adapted FPA (AFPA), to cope with UCTP in the previous work. However, AFPA suffers from the stagnation problem because of the non-diversity in the population. To improve the diversity of the population, this work introduces new Hybrid FPA with two variants: JFPA provided the Jaccard index to determine similarities among categorical data and the greedy selection mechanism to improve the selection of the random solution, and DFPA applied the navigational characteristics of the Dragonfly Algorithm (DA) to help in the neighborhood relationship. The results in this study indicate that the proposed algorithms have better exploration ability and fast convergence rate in comparison; JFPA outperforms AFPA in 3 out of 4 datasets for both small and large datasets, and DFPA outperforms AFPA and GA in all datasets while it outperforms PSO in 3 out of 4 small datasets and 2 complex large datasets.

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Dragonfly Algorithm Based Global Maximum Power Point Tracker for Photovoltaic Systems

Cited by 22 publications

References 9 publications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

New hybrid flower pollination algorithm with dragonfly algorithm and jaccard index to enhance solving university course timetable problem

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