Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

Joseph, X. Felix; Pradeep, Arun; Premkumar, M.; Kumar, C.

doi:10.1016/j.ijleo.2021.167973

Cited by 29 publications

(7 citation statements)

References 71 publications

(57 reference statements)

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“…In 25 , an improved TLBO (ITLBO) was emerged with different teaching tactics and performed in a comparative way on both SDM and DDM. In 26 , a grey Wolf algorithm having an orthogonal learning strategy has been manifested for finding the unknowns of different solar PV models. Considering the Triple-Junction (TJS) PV panel, moth search 27 , water cycle 28 and heap optimizer 29 techniques were applied for extracting the parameters of InGaP/InGaAs/Ge TJS PV panel.…”

Section: Introductionmentioning

confidence: 99%

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

El‐Sehiemy¹,

Shaheen²,

El‐Fergany³

et al. 2023

Sci Rep

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The parameter extraction of PV models is a nonlinear and multi-model optimization problem. However, it is essential to correctly estimate the parameters of the PV units due to their impact on the PV system efficiency in terms of power and current production. As a result, this study introduces a developed Artificial Hummingbird Technique (AHT) to generate the best values of the ungiven parameters of these PV units. The AHT mimics hummingbirds' unique flying abilities and foraging methods in the wild. The AHT is compared with numerous recent inspired techniques which are tuna swarm optimizer, African vulture’s optimizer, teaching learning studying-based optimizer and other recent optimization techniques. The statistical studies and experimental findings show that AHT outperforms other methods in extracting the parameters of various PV models of STM6-40/36, KC200GT and PWP 201 polycrystalline. The AHT’s performance is evaluated using the datasheet provided by the manufacturer. To highlight the AHT dominance, its performance is compared to those of other competing techniques. The simulation outcomes demonstrate that the AHT algorithm features a quick processing time and steadily convergence in consort with keeping an elevated level of accuracy in the offered solution.

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Section: Introductionmentioning

confidence: 99%

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

El‐Sehiemy¹,

Shaheen²,

El‐Fergany³

et al. 2023

Sci Rep

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“…Many metaheuristic algorithms have recently been reported in addition to the above-discussed algorithms for numerical and real-world engineering design optimization problems, including data clustering. For instance, ant colony optimization 35 , firefly algorithm 36 , 37 , flower pollination algorithm 38 , grey wolf optimizer (GWO) 39 – 42 , Jaya algorithm 43 , Teaching–learning based optimization (TLBO) algorithm 44 , Rao algorithm 45 , political optimizer 46 , whale optimization algorithm (WOA) 47 , Moth flame algorithm (MFO) 48 , multi-verse optimizer (MVO) 49 , Salp swarm algorithm (SSA) 50 , 51 , spotted hyena optimizer 52 , butterfly optimization 53 , lion optimization 54 , fireworks algorithm 55 , Cuckoo search algorithm 56 , bat algorithm 57 , Tabu search 58 , harmony search algorithm 59 , Newton–Raphson optimizer 60 , reptile search algorithm 61 , slime mould algorithm 62 , 63 , harris hawk optimizer 64 , Chimp optimizer 65 , artificial gorilla troop optimizer 66 , atom search algorithm 67 , marine predator algorithm 68 , 69 , sand cat swarm algorithm 70 , equilibrium optimizer 71 , 72 , Henry gas solubility algorithm (HGSA) 73 , resistance–capacitance algorithm 74 , arithmetic optimization algorithm 75 , quantum-based avian navigation optimizer 76 , multi trail vector DE algorithm 10 , 77 , arithmetic optimization algorithm 78 , starling murmuration optimizer 79 , atomic orbit search (AOS) 80 , subtraction-average-based optimizer 81 , etc. are reported for solving optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

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“…To get around problems with hypotheses and difficulty with the convergence of traditional iterative procedures, metaheuristic algorithms have been reported to identify the parameters of the PV cell/module 15,16,34‐36 . In general, several metaheuristic techniques, including Genetic Algorithm (GA), 37 Differential Evolutionary (DE) algorithm, 38 Artificial Bee Colony Algorithm, 39 ant colony optimization, 40 Particle Swarm Optimization (PSO), 12 Bat Algorithm, 41 Cuckoo Search Optimization Algorithm, 42 Bacterial Foraging Algorithm, 43 Pattern Search Algorithm (PSA), 44 Tabu Search Algorithm, 45 Harmony Search Algorithm, 46 Symbiotic Organisms Search (SOS) algorithm, 47 Sunflower Optimization Algorithm, 48 Gray Wolf Optimizer (GWO), 49‐51 hybrid GWO, 52 Salp Swarm Algorithm (SSA), 53‐55 Whale Optimization Algorithm, 56,57 Dragonfly Algorithm, 58 Firefly Optimization algorithm, 59 Fireworks Algorithm (FA), 60 Moth‐Flame Optimization (MFO) algorithm, 61 Multiverse Optimizer, 62 Sine‐Cosine Algorithm (SCA), 63 Ant Lion Optimizer (ALO), 64 Cat Swarm Algorithm (CSA), 65 JAYA algorithm, 66,67 Harris Hawk Optimizer (HHO), 31 Coyote Optimization Algorithm (COA), 68 Slime Mold Algorithm (SMA), 69‐71 RAO algorithm, 72,73 Atom Search Optimizer, 74 Manta Ray Foraging Algorithm, 32 Equilibrium Optimizer, 75,76 Spotted Hyena Algorithm, 77 Marine Predator Algorithm (MPA), 78 Arithmetic Optimization Algorithm (AOA), 79 Gradient‐Based Optimizer (GBO), 80‐82 Hunger Games Search Optimizer (HGSO), 83,84 Runge–Kutta Optimization Algorithm (RKOA), 85 thermal exchange optimizer, 86 Honey Badger Optimizer (HBO), 87 Jumping Spider Optimizer (...…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

Lakshmanan¹,

Kumar²,

Jasper

2023

Optim Control Appl Methods

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In the modeling and designing of PhotoVoltaic (PV) systems, parameter characterization in PV cell/module models remains a crucial field of research. Diode‐based models, such as single‐diode model (SDM), double‐diode model (DDM), and the three‐diode model, are frequently employed, and SDM and DDM are the most significant models. As a result, the difference between the estimated and experimental current can be minimized by using an objective function to solve the parameter characterization of such models. Metaheuristic optimization algorithms have recently been employed to get around the difficulty of finding accurate and highly reliable outcomes quickly. As a result, this research modifies the fundamental SDM and DDM and considers an objective function based on the modified models. Additionally, an improved version of a novel metaheuristic algorithm called White Shark Optimizer (WSO) is proposed by modifying the force control parameters of the WSO, and a chaotic generator is infused to improve the exploitation ability of WSO. The modified algorithm is named IWSO and applied to extract the PV parameters. This paper uses the new objective function to compare the conventional and the modified PV models. The outcomes of the experiment demonstrated IWSO's dominance over competing algorithms. With an average Freidman's ranking test value of 1.171, the proposed IWSO is superior to all selected algorithms. The average accuracy of modified SDM and DDM is 12% better than the traditional PV models. According to the findings, IWSO's estimated parameter values are the best, with the smallest difference between estimated and experimental current.

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Orthogonal learning-based Gray Wolf Optimizer for identifying the uncertain parameters of various photovoltaic models

Cited by 29 publications

References 71 publications

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Optimal parameter characterization of an enhanced mathematical model of solar photovoltaic cell/module using an improved white shark optimization algorithm

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