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

Abstract

Determining the optimal parameters for the photovoltaic system (PV) model is essential during the design, evolution, development, estimation, and PV systems analysis. Therefore, it is crucial for the proper advancement of the best parameters of the PV models based on modern computational techniques. Thus, this work suggests a new Orthogonal-Learning-Based Gray Wolf Optimizer (OLBGWO) through a local exploration for estimating the unknown variables of PV cell models. The exploitation and exploration capability of the basic Gray Wolf Optimizer (GWO) is improved by the orthogonal-learning-based (OLB) approach, and this arrangement promotes a highly reliable equilibrium between the exploitation and exploration levels of the algorithm. In OLBGWO, the OLB strategy is used to find the best solution for the poor populations and directs the population to review the potential search area during the iterative process. Also, an exponential decay function is employed to decrease the value of vector a in GWO. The developed algorithm is directly applied to the parameter identification problem of the PV system. The proposed OLBGWO algorithm estimates the unknown parameters of the single-diode model (SDM), double-diode model (DDM), and PV module model. The performance of the OLBGWO is compared with other competitive algorithms to prove its superiority. The simulation results prove that the OLBGWO algorithm can achieve high solution accuracy with high convergence speed.

Electrical parameters extraction of PV modules using artificial hummingbird optimizer

“…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.…”

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

“…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.…”

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

“…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 (...…”