Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods

Ridha, Hussein Mohammed; Hizam, Hashim; Mirjalili, Seyedali; Othman, Mohammad Lutfi; Ya’acob, Mohammad Effendy; Ahmadipour, Masoud

doi:10.1016/j.rser.2022.112436

Cited by 42 publications

(14 citation statements)

References 114 publications

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“…For the purpose of verifying the effectiveness of the suggested MOAOACS approach, a comparative analysis with competitive algorithms including AOACS, GCAOA NR (Ridha et al, 2022a), GCAOA LW (Ridha et al, 2022a), MPA LW (Ridha, 2020), ELPSO NR (Ridha et al, 2022a), MPSO NR (Merchaoui et al 2018), MRFO NR (Houssein et al 2021), NSCSO (Gude et al, 2022), and DEMO (Muhsen et al, 2016) methods is also conducted to assess single-diode PV module parameters. Suggested approach and all competitive methods are utilized to obtain SDM PV module parameters via optimizing RMSE subjected to experimental I-V data set.…”

Section: Evaluatormentioning

confidence: 99%

“…Similarly, different artificial intelligence-based methods (AI) are used to minimize the difference between a referenced PV module parameters and model's output. In (Siddiqui and Abido, 2013; Zagrouba et al, 2010; Ismail et al, 2013; Harrag and Messalti, 2017; Fathy et al, 2019; Qais et al, 2020; Chenouard and El-Sehiemy, 2020; Oulcaid et al, 2020; Premkumar et al, 2020; Ridha et al, 2022a; Ridha et al, 2022b; Pan et al, 2022; Farah et al, 2022; Sharma et al, 2022; Wang et al, 2022), many AI-based algorithms were used as well to solve the same objective function including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), DE-assisted tabu search (TS), (TSDE), PSO assisted DE, enhanced moth search algorithm (EMSA), the interval branch and bound (IBEXOPT) algorithm, Grey wolf Optimizer (GWO), Symbiosis organisms search (SOS), and dynamic opposite learning strategy (DOL).…”

Section: Introductionmentioning

confidence: 99%

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A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

Almunem,

Muhsen,

Haider

et al. 2023

Energy Exploration & Exploitation

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In this research, hybrid method is proposed to model the I–V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I–V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I–V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I–V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I–V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.

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Section: Evaluatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

Almunem,

Muhsen,

Haider

et al. 2023

Energy Exploration & Exploitation

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show abstract

“…To accelerate convergence and improve the exploration potential of metaheuristic algorithms, learning mechanisms, chaotic drifts, and adaptive weight mechanisms are added to the core algorithms. [14], [15], [16], [17], [18], [19]. Adding features such as an adaptive mutation operator [2], pattern search [20], ranking mechanism [21], new population updating strategy [22], reinforcement learning and so on increases the algorithm's performance.…”

Section: Literature Surveymentioning

confidence: 99%

Parameter Estimation of Solar PV Models with an Artificial Humming Bird Optimization Algorithm using Various Objective Functions

Ayyarao¹,

kishore²

2022

Preprint

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Accurate photovoltaic (PV) models are essential to optimize grid operations and dynamic energy management. This article proposes parameter estimation of solar PV models using Artificial Humming bird Optimization (AHO) algorithm. The AHO algorithm is inspired by hummingbird flight dynamics and mimics hummingbird foraging behaviour. Three objective functions are developed to minimize the root mean square difference between the experimental and estimated currents. The first objective function is based on the conventional RMSE, while the second is developed using the Lambert W function, and the third is developed using the iterative Newton Raphson approach. The proposed AHO algorithm is applied to estimate the parameters for a single diode model (SDM), a double diode model (DDM), and a PV module. The AHO algorithm achieves an RMSE of 7.2985E-04 for SDM and 7.4080E-04 for DDM. The proposed AHO algorithm's performance is compared to the findings of other algorithms reported in the literature.

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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]. Table 1 summarizes the main findings through the last 2 years.…”

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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Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods

Cited by 42 publications

References 114 publications

A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

A novel hybrid method for modeling of photovoltaic module I–V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

Parameter Estimation of Solar PV Models with an Artificial Humming Bird Optimization Algorithm using Various Objective Functions

Photovoltaic model parameters identification using an innovative optimization algorithm

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