Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm

Sharma, Ashutosh; Saxena, Akash; Shekhawat, Shalini; Kumar, Rajesh; Mathur, Abhishek

doi:10.1007/978-981-15-5495-7_20

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…Harris Hawks Optimization Algorithm (HHO, 2020) [37] The application of HHO to estimate the unknown parameters of the PV models has been recently proposed to examine its efficiency in comparison to some of the other optimization algorithms. The experimental findings show the efficiency of HHO over the compared ones.…”

Section: Algorithm and Year Contributions And Limitationsmentioning

confidence: 99%

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Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

et al. 2021

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Currently, the incorporation of solar panels in many applications is a booming trend, which necessitates accurate simulations and analysis of their performance under different operating conditions for further decision making. In this paper, various optimization algorithms are addressed comprehensively through a comparative study and further discussions for extracting the unknown parameters. Efficient use of the iterations within the optimization process may help meta-heuristic algorithms in accelerating convergence plus attaining better accuracy for the final outcome. In this paper, a method, namely, the premature convergence method (PCM), is proposed to boost the convergence of meta-heuristic algorithms with significant improvement in their accuracies. PCM is based on updating the current position around the best-so-far solution with two-step sizes: the first is based on the distance between two individuals selected randomly from the population to encourage the exploration capability, and the second is based on the distance between the current position and the best-so-far solution to promote exploitation. In addition, PCM uses a weight variable, known also as a controlling factor, as a trade-off between the two-step sizes. The proposed method is integrated with three well-known meta-heuristic algorithms to observe its efficacy for estimating efficiently and effectively the unknown parameters of the single diode model (SDM). In addition, an RTC France Si solar cell, and three PV modules, namely, Photowatt-PWP201, Ultra 85-P, and STM6-40/36, are investigated with the improved algorithms and selected standard approaches to compare their performances in estimating the unknown parameters for those different types of PV cells and modules. The experimental results point out the efficacy of the PCM in accelerating the convergence speed with improved final outcomes.

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Section: Algorithm and Year Contributions And Limitationsmentioning

confidence: 99%

“…For the nonstandard conditions unlike STC, the above-stated mathematical model should be altered to show the performances under varied cell temperatures and changed radiation levels. The equation's model should be adapted to accommodate such changes due to G and T variations as follows [6,[35][36][37]43,44]:…”

Section: Photovoltaic (Pv) Module Modelmentioning

confidence: 99%

Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

et al. 2021

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“…For this purpose, this paper proposes an improved version of the Northern Goshawk Optimization algorithm, named SR-NGO, based on the sine cosine guiding mechanism and random learning mechanism. A comparative analysis is performed with other algorithms such as CIWPSO [13] , DE [14] , CSA [15] , HHO [16] , SSA [17] , and RUN [18] . The accuracy, stability, and convergence speed of parameter identification are evaluated using the root mean square error, absolute error, and convergence curves.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of solar cell model based on improved northern goshawk optimization algorithm

Xiao,

Zhou,

et al. 2024

Preprint

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In this study, an improved northern goshawk optimization algorithm called SR-NGO is developed to solve the parameter identification problem of the Single Diode Model (SDM), Double Diode Model (DDM), and Triple Diode Model (TDM). Compared to the NGO algorithm, the SR-NGO algorithm reduces the minimum, maximum, mean, and standard deviation values of the root mean square error between experimental I-V data and the SDM(DDM, TDM) fitting data by 0.0004284% (0.2542%, 0.2034%), 1.1094% (3.7313%, 6.0740%), 0.05106% (0.6901%, 1.0699%), and 99.9999% (88.1540%, 94.0600%), respectively. Furthermore, the SR-NGO algorithm exhibits higher accuracy, faster convergence speed, and stronger stability compared to other existing algorithms. These results demonstrate that the SR-NGO algorithm, which combines the sine cosine guiding mechanism and random learning mechanism, enhances both local and global search capabilities, thereby improving the ability to escape local optima.

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“…For reaching optimum solutions to the SPV parameters estimation problem, several metaheuristic techniques have been developed and utilized, which include among others; Moth Flame Optimization (MFO) (Mirjalili, 2015), Dragonfly Algorithm (DA) (Isa et al, 2020), Whale Optimization Algorithm (WOA) (Xiong et al, 2018), Grey Wolf Optimization (GWO) (Darmansyah & Robandi, 2017), Ant Lion Optimization (ALO) (Kanimozhi & Kumar, 2018), Harris Hawk Optimization (HHO) (Sharma et al, 2021), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO) (Premkumar et al, 2021), Marine Predator Algorithm (MPA) (Sattar et al, 2021), and African Vultures Optimization Algorithm (AVOA) (Kumar & Mary, 2021). Each one of these techniques has different strategies for achieving a specific main goal, and the power of each technique depends upon the accuracy of the estimated unknown parameters, and computation time with less complexity.…”

Section: Introductionmentioning

confidence: 99%

Coot Optimization Algorithm for Parameter Estimation of Photovoltaic Model

Rushdi¹,

Al-Naima²

2022

MEST

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Because of the technical and environmental advantages of many solar energy sources, their use has recently been rapidly rising. The extraction of the unknown parameters in photovoltaic models is one of the key challenges in the modeling and simulation of solar energy sources. To satisfy the behavior of the solar photovoltaic (SPV) cells, the Single-Diode Model (SDM) is recommended as a more dependable modeling method. In this study, we applied the recently introduced meta-heuristic optimization method that inspires the behavior of the swarm of birds called Coot. This Coot Algorithm is used to estimate the unknown parameters of an SPV cell/module at 33 ºC. Simulation results of this study were compared with other nine different previous Optimization Algorithms, which are Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vultures Optimization Algorithm (AVOA). The obtained results of this comparison showed that the Coot algorithm outperformed the previous algorithms in terms of the root mean square error (RMSE) and the degree of convergence between the power versus voltage curve and the current versus voltage curve compared with the measured data. Moreover, the results confirm that the Coot optimization algorithm is favorable in reducing time and improving accuracy.

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Solar Cell Parameter Extraction by Using Harris Hawks Optimization Algorithm

Cited by 7 publications

References 26 publications

Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

Recent Meta-Heuristic Algorithms with a Novel Premature Covergence Method for Determining the Parameters of PV Cells and Modules

Parameter identification of solar cell model based on improved northern goshawk optimization algorithm

Coot Optimization Algorithm for Parameter Estimation of Photovoltaic Model

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