Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction

Song, Shiming; Wang, Pengjun; Heidari, Ali Asghar; Zhao, Xuehua; Chen, Huiling

doi:10.1016/j.engappai.2021.104608

Cited by 59 publications

(10 citation statements)

References 127 publications

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“…The third check utilized Equation (13), which also includes an exponential factor. In the case of maximum power, the diode current I d , calculated with the exponential function and I o , was a smaller part of the overall current compared to the open circuit case.…”

Section: Current Check At Maximum Powermentioning

confidence: 99%

Systems Engineering Methodology for Verification of PV Module Parameter Solutions

2023

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Numerous sources provide methods to extract photovoltaic (PV) parameters from PV module datasheet values. The inputs are the number of series cells N s , open circuit voltage V oc , maximum power voltage V mp , maximum power current I mp , and short circuit current I sc . The 5 Parameter Model solutions outputs are diode ideality factor η, series resistance R s , parallel resistance R p , photon light current I L , and diode reverse saturation current I o . The parameter solution requires solving three simultaneous transcendental equations for η, R s , and R p and additional calculations for I L and I o . One of the primary tenants of Systems Engineering, verification, was applied to parameter solution results to check for physical and model fitness. This manuscript provides novel methods to verify parameter results and applies them to available solutions.

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Section: Current Check At Maximum Powermentioning

confidence: 99%

Systems Engineering Methodology for Verification of PV Module Parameter Solutions

2023

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“…For instance, Kaveh et al [16] proposed an efficient hybrid method based on the HHO and the imperialist competitive algorithm, compensating for HHO's poor performance in searching for optimal solutions. Song et al [17] identified HHO's deficiency in global search capability and proposed the persistent-trigonometric-differences mechanism to improve its global search capability, while also enhancing the energy factor to better balance the algorithm's exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

Augmented Harris Hawks Optimizer with Gradient-Based-like Optimization: Inverse Design of All-Dielectric Meta-Gratings

Edee

2023

Biomimetics

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In this paper, we introduce a new hybrid optimization method for the inverse design of metasurfaces, which combines the original Harris hawks optimizer (HHO) with a gradient-based optimization method. The HHO is a population-based algorithm that mimics the hunting process of hawks tracking prey. The hunting strategy is divided into two phases: exploration and exploitation. However, the original HHO algorithm performs poorly in the exploitation phase and may get trapped and stagnate in a basin of local optima. To improve the algorithm, we propose pre-selecting better initial candidates obtained from a gradient-based-like (GBL) optimization method. The main drawback of the GBL optimization method is its strong dependence on initial conditions. However, like any gradient-based method, GBL has the advantage of broadly and efficiently spanning the design space at the cost of computation time. By leveraging the strengths of both methods, namely GBL optimization and HHO, we show that the proposed hybrid approach, denoted as GBL–HHO, is an optimal scenario for efficiently targeting a class of unseen good global optimal solutions. We apply the proposed method to design all-dielectric meta-gratings that deflect incident waves into a given transmission angle. The numerical results demonstrate that our scenario outperforms the original HHO.

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“…Song et al. [ 3 ] identified the deficiency of the global search capability of the Harris Hawk Optimizer, and they proposed the persistent-trigonometric-differences mechanism to improve the global search capability of the HHO; in addition, they improved the energy factor of the original algorithm to better balance the exploration and exploitation of the algorithm; finally, they applied it to the parameter identification problem of photovoltaic model parameter extraction. Zhong et al.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection

Zou

Zhou

2022

Entropy

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To overcome the lack of flexibility of Harris Hawks Optimization (HHO) in switching between exploration and exploitation, and the low efficiency of its exploitation phase, an efficient improved greedy Harris Hawks Optimizer (IGHHO) is proposed and applied to the feature selection (FS) problem. IGHHO uses a new transformation strategy that enables flexible switching between search and development, enabling it to jump out of local optima. We replace the original HHO exploitation process with improved differential perturbation and a greedy strategy to improve its global search capability. We tested it in experiments against seven algorithms using single-peaked, multi-peaked, hybrid, and composite CEC2017 benchmark functions, and IGHHO outperformed them on optimization problems with different feature functions. We propose new objective functions for the problem of data imbalance in FS and apply IGHHO to it. IGHHO outperformed comparison algorithms in terms of classification accuracy and feature subset length. The results show that IGHHO applies not only to global optimization of different feature functions but also to practical optimization problems.

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Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction

Cited by 59 publications

References 127 publications

Systems Engineering Methodology for Verification of PV Module Parameter Solutions

Systems Engineering Methodology for Verification of PV Module Parameter Solutions

Augmented Harris Hawks Optimizer with Gradient-Based-like Optimization: Inverse Design of All-Dielectric Meta-Gratings

An Efficient Improved Greedy Harris Hawks Optimizer and Its Application to Feature Selection

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