Modeling of solar cell under different conditions by Ant Lion Optimizer with LambertW function

Kanimozhi, G.; Kumar, Harish

doi:10.1016/j.asoc.2018.06.025

Cited by 81 publications

(13 citation statements)

References 31 publications

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“…As an alternative to the deterministic techniques, the naturally inspired (meta-heuristic) are extensively used in the last decade. In literatures, there are a lot of these methods such as, biogeography-based heterogeneous cuckoo search (BHCS) [18], pattern search (PS) [19], firefly algorithm (FA) [20], ant lion optimizer (ALO) [21], Jaya algorithm [22], salp swarm algorithm (SSA) [23], elephant herd optimizer [24], enhanced sine cosine algorithm (ISCA) [25], hybridized interior search algorithm (HISA) [26], an artificial bee colony-differential evolution (ABC-DE) [27], improved adaptive Nelder-Mead simplex (NMS) hybridized with the artificial bee colony (ABC) metaheuristic, algorithm of hybrid adaptive and Nelder-Mead simplex (EHA-NMS) [28], mutative-scale parallel chaos optimization algorithm (MPCOA) [29], classified perturbation mutation based particle swarm optimization (CPMPSO) [30], heterogeneous comprehensive learning particle swarm optimizer (HCLPSO) [31], and improved shuffled complex evolution (ISCE) [32]. A forensic based optimization algorithm was developed in [33] for finding the optimal parameters of solar cell modules.…”

Section: Introductionmentioning

confidence: 99%

Identification study of solar cell/module using recent optimization techniques

El‐Dabah

El‐Sehiemy

Ebrahim

et al. 2022

IJECE

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This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

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Section: Introductionmentioning

confidence: 99%

Identification study of solar cell/module using recent optimization techniques

El‐Dabah

El‐Sehiemy

Ebrahim

et al. 2022

IJECE

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“…Conversely, the numerical (metaheuristic) evolutionary and hybrid algorithms are capable of escaping from local optima and reaching the global optimum solution easily. As per the literature, there are many metaheuristic optimization algorithms used in the estimation of PV parameters estimation, such as: Particle Swarm Optimization (PSO) [12], Artificial Bee Colony (ABC) [13], Real Coded Genetic Algorithm (RCGA) [14], Cuckoo Search (CS) [15] , Biogeography-based Heterogeneous Cuckoo Search (BHCS) [16], Firefly Algorithm (FA) [17], Moth-Flame Optimization Algorithm (MFOA) [18], Bee Pollinator Flower Pollination Algorithm (BPFPA) [19], Pattern Search (PS) [20], Harmony Search (HS) [21], Fish Swarm Algorithm (FSA) [22], Ant Lion Optimizer (ALO) [23], Water Cycle Algorithm (WCA) [24], Jaya algorithm [25], Hybridized Interior Search Algorithm (HISA) [26], Artificial Immune System (AIS) [27], Salp Swarm Algorithm (SSA) [28], Artificial Biogeography based Optimization Algorithm with Mutation (BOA-M) [29], Elephant Herd Algorithm (EHA) [30], an Artificial Bee Colony-Differential Evolution (ABC-DE) [31], improved adaptive Nelder-Mead Simplex(NMS) hybridized with ABC algorithm, hybrid EHA-NMS [32], Improved Adaptive DE (IADE) [33], Chaotic Asexual Reproduction Optimization (CARO) [34], Improved Shuffled Complex Evolution (ISCE) [35], Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO) [36], Mutative-scale Parallel Chaos Optimization Algorithm (MPCOA) [37], Artificial Ecosystem optimization [38], Marine Predators Algorithm (MPA) [39], Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm [40], Coyote Optimization Algorithm (COA) [41], Harris Hawk Optimization (HHO) [42], Sunflower Optimization (SFO)…”

Section: Introductionmentioning

confidence: 99%

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

et al. 2021

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In this paper, a modified version of a recent optimization algorithm called gradient-based optimizer (GBO) is proposed with the aim of improving its performance. Both the original gradient-based optimizer and the modified version, MGBO, are utilized for estimating the parameters of Photovoltaic models. The MGBO has the advantages of accelerated convergence rate as well as avoiding the local optima. These features make it compatible for investigating its performance in one of the nonlinear optimization problems like Photovoltaic model parameters estimation. The MGBO is used for the identification of parameters of different Photovoltaic models; single-diode, double-diode, and PV module. To obtain a generic Photovoltaic model, it is required to fit the experimentally obtained data. During the optimization process, the unknown parameters of the PV model are used as a decision variable whereas the root means squared error between the measured and estimated data is used as a cost function. The results verified the fast conversion rate and precision of the MGBO over other recently reported algorithms in solving the studied optimization problem.

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“…Notably, the analysis techniques are predicated on a few main points on I-V curves and a sequence of computations [42,43], which are straightforward but lack accurate results under varying environmental conditions because such points are only inferred under STCs. Deterministic methods such as iterative curve fitting [44], Newton-Raphson (NR) method [45][46][47], and Lambert W-functions [48][49][50] can be more accurate. However, they are pretty stringent with model characteristics and hypersensitive to initial operation conditions, making them susceptible to falling into an optimal local state when dealing with problems involving many different modes.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, many researchers still compute the solar cell current without bearing in mind its nonlinearity or the variation of each variable, resulting in inaccurate results. Many approaches, such as the Taylor series (TS) [86], the f-solve [87], the NR [45,46], and the Lambert W-function [48,88], have been realized to handle with circuit relations to overcome this difficulty efficiently. On the other hand, the TS technique has the disadvantage of taking longer and achieving low convergence than the NR [79].…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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Parameter identification and accurate photovoltaic (PV) modeling from basic I-V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multidimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton-Raphson (NR) technique is discussed to solve the chaotic behavior of the I-V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman's test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

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Modeling of solar cell under different conditions by Ant Lion Optimizer with LambertW function

Cited by 81 publications

References 31 publications

Identification study of solar cell/module using recent optimization techniques

Identification study of solar cell/module using recent optimization techniques

A Novel Solution Methodology Based on a Modified Gradient-Based Optimizer for Parameter Estimation of Photovoltaic Models

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

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