Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Saadaoui, Driss; Elyaqouti, Mustapha; Assalaou, Khalid; hmamou, Driss Ben; Lidaighbi, Souad

doi:10.1016/j.ecmx.2021.100129

Cited by 39 publications

(29 citation statements)

References 74 publications

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“…Throughout the past few years, researchers have used a variety of meta‐heuristic optimization approaches for the proposed problem, such as the Real Coded Genetic algorithm (RCGA) [24], Salp Swarm Algorithm [25], Crow search algorithm (CSA) [26], Particle swarm optimization [27], harmony search‐based algorithms [28], Firefly algorithm [29], Artificial bee colony [30], Cuckoo algorithm [31], Crow Whale optimization algorithm [32], A Genetic Algorithm Based on The Non‐Uniform Mutation [33], Directional Permutation Differential, Evolution Algorithm [34], Hybrid Grey Wolf Optimization and Cuckoo Search Algorithm [35], Biogeography Based Optimization [36], Enhanced JAYA [37], Brain Storming Optimization algorithm [38], Transient Search Optimization [39], Hybridized interior search algorithm [40], hybrid differential evolution with whale optimization algorithm [41]. Electromagnetic‐like Algorithm [42], Moth Search Algorithm [43], trust‐region‐reflective technique [44], shuffled frog leaping algorithm [45], Gradient‐based optimizer [46], Simplex simplified swarm optimization [47], Improved gradient‐based optimizer [48], Artificial ecosystem‐based optimization (AEO) [49, 50], Simplified swarm optimization [51], hybrid African vultures–grey wolf optimizer [52], modified social network search algorithm combined with the Secant method [53], improved stochastic fractal search [54], Random learning gradient‐based optimizer [55], comprehensive learning Rao‐1 [56], differential evolution [57‐59], arithmetic optimization algorithm [60], Fractional Chaotic Ensemble Particle Swarm Optimizer [61], supply–demand optimizer [62], Runge Kutta based optimization (RUN) [63].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic model parameters identification using an innovative optimization algorithm

El‐Dabah

El‐Sehiemy

Hasanien

et al. 2023

IET Renewable Power Gen

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As it tackles electrical and non‐electrical losses, the triple‐diode model (TDM) of photovoltaic (PV) cells is highly exact. This paper employs a novel optimization method known as the innovative optimization algorithm (INFO) technique to correctly estimate the electrical characteristics of such TDM. To shift agents towards a better position, the INFO algorithm exploits the concept of weighted mean. The primary goal of INFO is to stress its performance features to solve some optimization difficulties that other approaches cannot effectively solve. In this paper, the objective function based on a combination of the absolute value of the current error, its squared value, and its quadrable value is employed, which the INFO optimizer minimizes to predict the optimum parameters of such TDM precisely. The proposed INFO algorithm is carried out on multi‐ and mono‐crystalline varieties, such as the Kyocera KC200GT and the Canadian Solar CS6K‐280 M. The simulation outcomes demonstrate the INFO's ability to extract the model parameters precisely. The INFO achieved the lowest ideal fitness values of 9.0738 × 10−06 and 5.7356 × 10−05 for the KC200GT and Canadian Solar CS6K‐280 M, respectively, throughout the optimization procedure. Under various environmental circumstances, experimental validation of the calculated parameters using the (INFO) optimizer is carried out, and the results are compared to the observed values from the laboratory experiments. The simulation results demonstrate the INFO's convergence time and accuracy advantage over competing optimization techniques. Additionally, statistical analysis shows that the INFO optimizer is resilient.

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Section: Introductionmentioning

confidence: 99%

Photovoltaic model parameters identification using an innovative optimization algorithm

El‐Dabah

El‐Sehiemy

Hasanien

et al. 2023

IET Renewable Power Gen

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“…As a consequence, popular metaheuristic methods have been proposed to obtain an accurate solution in less time. For instance, the performance of electrolytes in the solar cell can also be investigated by soft computing methods 8 …”

Section: Introductionmentioning

confidence: 99%

“…For instance, the performance of electrolytes in the solar cell can also be investigated by soft computing methods. 8 Numerous metaheuristic techniques have been adopted that includes genetic algorithm, 9 artificial immune system, 10 differential evolution (DE), 11,12 Metaphor-free dynamic spherical evolution, 13 artificial bee swarm optimization (ABSO), 14 particle swarm optimization (PSO), 15 enhanced leader particle swarm optimization (ELPSO), 16 time-varying acceleration coefficients particle swarm optimization (TVACPSO), 17 Random reselection PSO, 18 Gravitational search algorithm, 19 harmony search (HS), 20,21 simulated annealing (SA), 22 memetic algorithm (MA), 6 pattern search (PS), 23 cuckoo search (CS), 24 biogeographybased optimization (BBO) with mutation formulations, 25 artificial bee colony optimization (ABCO), symbiotic organisms search (SOS), 26,27 modified artificial bee colony optimization (MABCO), 28 teaching-learning-based optimization (TLBO), 29,30 bird mating optimizer (BMO), 31 Grey wolf optimizer (GWO), 32,33 war strategy optimization algorithm, 34 improved arithmetic optimization algorithm, 35 Laplacian Nelder-Mead spherical evolution, 36 ensemble multi-strategy shuffled frog leading algorithms, 37 Delayed dynamic step shuffling frog-leaping algorithm, 38 boosted LSHADE algorithm and Newton Raphson method, 39,40 Boosting slime mould algorithm, 41 Gradient-based optimization with ranking mechanisms, 42 etc., for the non-linear parameter extraction optimization problem. Although these metaheuristic techniques yield better approximate solutions, every algorithm has its respective limitations.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid approach for the optimization of double‐diode model parameters of solar cell

Kanimozhi

Kumar

Satyanarayana

2022

Intl J of Energy Research

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Summary In the present work, the intrinsic parametric quantities of the solar cell are attempted to determine by the proposed Lambert W‐based double‐diode model as a novel hybrid approach. The proposed hybrid approach is tested with three metaheuristic algorithms. The solar cell double‐diode model parameters are optimized by three different metaheuristic methods viz., Ant Lion Optimizer, firefly algorithm, and sine‐cosine algorithm. The conventional solar cell model equation is non‐linear in nature. An equivalent equation in terms of the Lambert W function is used for the optimization process. The performance of these three methods is compared by their respective root‐mean‐squared‐error value, convergence process, and optimization time. The proposed hybrid approach method of optimization has yielded good RMSE values in the order of 10−3 for the three tested metaheuristic algorithms. It is found that among the three proposed methods, Lambert W‐based sine‐cosine algorithm outperforms the other two methods and is considered to be a promising technique for investigating photovoltaic performance.

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“…They have concluded that the proposed algorithm is the best one compared to other algorithms for two case studies. In [46], to generate solar cells and PV modules parameters, the authors have suggested a genetic algorithm based on nonuniform mutation (GAMNU). The performance of the method is approved using diverse PV models and modules.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using Artificial Hummingbird Algorithm

et al. 2022

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In this article, an artificial algorithm called hummingbirds optimization method, named AHA algorithm, is settled to extract accurately the parameters of PV modules under outdoor operational conditions. AHA is the main contribution in this work, regarding its efficiency and good performance in terms of standard deviation (StD), root mean square error (RMSE), sum of squared error (SSE), maximum number of iterations (MaxIt), particularly, for extracting parameter from modules operating in real conditions, and under different temperature and irradiance levels. The AHA is applied on a Polycrystalline-solar panel module type 320W-72P at real operating conditions and on a PV array of three polycrystalline PV modules connected in series. PV cells of the same modules are assumed operating under the same conditions where they share the same electric current and voltage values. In this last pattern, eight different scenarios are chosen. In the first five scenarios, the temperatures are (46.97, 44.23, 42.87, 40.59 and 30.60°C), and the irradiance varies, such as (910, 800.57, 614.13, 415.1 and 200.87W/m2), respectively. In the other three testing scenarios, the solar irradiance is equal (803 W/ m2), and the temperature differs, such as T=47. 93 °C, 53.38 °C and 36.62°C. Lower values of root mean square errors (RMSE) are achieved (9.8602 × 10 −4 and 2.572533 × 10 −2 for RTC France PV cell and the 320W-72P module respectively) with 6000 iterations. Moreover, all the eight scenarios are lower than 7. 245916 × 10 −2 for the last case study. Moreover, results show that we could recommend the AHA algorithm as an advanced and efficient method for dealing with real time parameter optimization of photovoltaic modules. In fact, a high closeness between the simulated and the experimental curves is achieved, which indicates the perfectness of this optimization method. Finally, the proposed AHA algorithm can be engaged as tools for the best designing of PV systems.

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Parameters optimization of solar PV cell/module using genetic algorithm based on non-uniform mutation

Cited by 39 publications

References 74 publications

Photovoltaic model parameters identification using an innovative optimization algorithm

Photovoltaic model parameters identification using an innovative optimization algorithm

A novel hybrid approach for the optimization of double‐diode model parameters of solar cell

Parameter Estimation of Solar Modules Operating Under Outdoor Operational Conditions Using Artificial Hummingbird Algorithm

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