Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review

Houssein, Essam H.; Zaki, Gamela Nageh; Abualigah, Laith; Younis, Eman M. G.

doi:10.1007/978-3-030-99079-4_7

Cited by 5 publications

(2 citation statements)

References 125 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, metaheuristic algorithms appeared in the 1980s [9], opened up promising new perspectives thanks to their strengths for solving NP-Hard problems [10] particularly for discrete problems where we are confronted with the explosion combinatorial. These algorithms, mostly inspired from the nature, mimic the human behavior, physics, biology and animal swarm intelligence [11][12][13][14][15][16][17][18][19][20][21]. The qualities of metaheuristics are very well defined by Osman, I.H.…”

Section: Introductionmentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

Faced with the increase in high-dimensional Big Data creating more volume and complexity, the feature selection process became an essential phase in the preprocessing workflow upstream of the design of systems based on deep learning. This paper is a concrete and first application of the new metaheuristic Harris Hawk Optimization Encirclement-Attack-Synergy (HHO-EAS) in solving the NP-Hard wrapper feature selection multi-objective optimization problem. This problem combines two contradictory objectives: maximizing the accuracy of a classifier while minimizing the number of the most relevant and non-redundant selected features. To do this we hybridized HHO-EAS to create the new metaheuristic Binary HHO-EAS (BHHO-EAS). We combined HHO-EAS to the sixteen transfer functions most used in the literature structured in a balanced way among the four main categories including S-Shaped, V-Shaped, Q-Shaped and U-Shaped. This wide range of transfer function allows us to analyze the evolution of BHHO-EAS’s skills according to the assigned transfer function and to determine which of them offer the best performances. We applied wrapper feature selection to the well-known NSL-KDD dataset with the deep learning Multi Layer Perceptron (MLP) classifier. We put BHHO-EAS in competition with three other well-known population based binary metaheuristics, BPSO, BBA and BHHO. The analysis of the experimental results, compared to the three other binary metaheuristics, demonstrated that BHHO-EAS obtained the best performance on 100% of the transfer functions. This is more particularly highlighted by the U-Shaped transfer functions, which give an acceptable compromise for the two objectives of the problem with an average accuracy of 96,4% and an average size of selected features of 20.

show abstract

Section: Introductionmentioning

confidence: 99%

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

SASSI,

CHELOUAH

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…As a direct consequence of this, efforts are currently being undertaken to create more effective optimization algorithms to calculate the parameters of solar photovoltaic cells [31,32]. In the next parts, an in-depth discussion on optimizing photovoltaic systems will be held, focusing on previously developed and evaluated methods.…”

Section: Introductionmentioning

confidence: 99%

A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

Mahmood,

Hussein,

Aljohani

et al. 2023

Mathematics

View full text Add to dashboard Cite

Harnessing solar energy efficiently via photovoltaic (PV) technology is pivotal for future sustainable energy. Accurate modeling of PV cells entails an optimization problem due to the multimodal and nonlinear characteristics of the cells. This study introduces the Multi-strategy Gradient-Based Algorithm (MAGBO) for the precise parameter estimation of solar PV systems. MAGBO incorporates a modified gradient search rule (MGSR) inspired by the quasi-Newton approach, a novel refresh operator (NRO) for improved solution quality, and a crossover mechanism balancing exploration and exploitation. Validated through CEC2021 test functions, MAGBO excelled in global optimization. To further validate and underscore the reliability of MAGBO, we utilized data from the PVM 752 GaAs thin-film cell and the STP6-40/36 module. The simulation parameters were discerned using 44 I-V pairs from the PVM 752 cell and diverse data from the STP6-40/36 module tested under different conditions. Consistency between simulated and observed I-V and P-V curves for the STM6-40/36 and PVM 752 models validated MAGBO’s accuracy. In application, MAGBO attained an RMSE of 9.8 × 10−4 for double-diode and single-diode modules. For Photowatt-PWP, STM6-40/36, and PVM 752 models, RMSEs were 2.4 × 10−3, 1.7 × 10−3, and 1.7 × 10−3, respectively. Against prevalent methods, MAGBO exhibited unparalleled precision and reliability, advocating its superior utility for intricate PV data analysis.

show abstract

HHO-EAS: a new metaheuristic bio-inspired of the win–win hunting synergy between the two predators crow and wolf

Sassi

Chelouah

2023

Artif Intell Rev

View full text Add to dashboard Cite

Metaheuristics for Parameter Estimation of Solar Photovoltaic Cells: A Comprehensive Review

Cited by 5 publications

References 125 publications

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

BHHO-EAS metaheuristic applied to the NP-Hard wrapper feature selection multi-objective optimization problem

A Modified Gradient Search Rule Based on the Quasi-Newton Method and a New Local Search Technique to Improve the Gradient-Based Algorithm: Solar Photovoltaic Parameter Extraction

HHO-EAS: a new metaheuristic bio-inspired of the win–win hunting synergy between the two predators crow and wolf

Contact Info

Product

Resources

About