Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization

Yu, Sudan; Heidari, Ali Asghar; Liang, Guoxi; Chen, Chi; Chen, Huiling; Shao, Qike

doi:10.1016/j.ijleo.2021.168513

Cited by 33 publications

(12 citation statements)

References 112 publications

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“…Several experiments were considered to verify the proposed BSCSOA's performance. The effectiveness of the BSCSOA is contrasted with the other seven algorithms, GWO, Improved JAYA (IJAYA), 39 GNDO, HHO, TWFOA, Opposition‐Based Learning GBO (OBLGBO) algorithm, 59 and SCSOA. The simulation tests in this section were run on a Laptop with Windows 11 (64‐bit) with an Intel(R) Core (TM) i5‐10300H CPU @ 2.50GHz clock frequency and 16 GB RAM.…”

Section: Resultsmentioning

confidence: 99%

“…Even though metaheuristic approaches are more reliable and efficient at finding solutions, their effectiveness is highly reliant on the appropriate selection of control variables 12 . Few instance of metaheuristic methods, including, Differential Evolutionary (DE) algorithms and its variants, 22,23 Genetic Algorithm (GA) and its variants, 24,25 Particle Swarm Optimizer (PSO) and its variants, 26,27 Artificial Bee Colony (ABC), 28 Ant Colony Optimization (ACO), 29 Water Cycle Algorithm (WCA), 30 Cuckoo Search Algorithm (CSA) and its variants, 31,32 Grey Wolf Optimizer (GWO), 33 Whale Optimizer (WO), 34 Firefly Optimizer (FFO), 35 Flower Pollination Algorithm (FPA), 36 Wind Driven Optimization (WDO), 37 Crow Search Algorithm (CrSA), 38 Jaya algorithm and its variants, 39,40 Shuffled Frog Leaping Algorithm (SFLA), 41 Symbiotic Organisms Search (SOS), 42 Salp Swarm Algorithm (SSA), 43–45 Emperor Penguin Algorithm (EPA), 46 Spotted Hyena Algorithm (SHA), 47 Ant Lion Optimizer (ALO), 48 Marine Predator Algorithm (MPA), 49,50 Equilibrium Optimizer (EO), 51,52 Teaching‐Learning‐Based Optimization (TLBO) algorithm, 53 Fireworks Algorithm (FA), 54 Slime Mould Optimization (SMA), 55,56 Runge–Kutta Optimizer (RKO), 57 Hunger Games Search Optimization Algorithm (HGSO), 14,58 Gradient‐Based Optimizer (GBO), 59–61 Tuna Swarm Optimizer (TSO), 62 Atom Search Optimizer (ASO), 63 Arithmetic Optimization Algorithm (AOA), 64 Jumping Spider Algorithm (JSA), 65 Plasma Generation Optimization (PGO), 66 Generalized Normal Distribution Optimization (GNDO) algorithm, 67 African Vulture Algorithm (AVA), 68 Thermal Exchange Optimization (TEO), 69 Turbulent Water Flow Optimization Algorithm (TWFOA), 70 etc. and improvement techniques, such as Nelder–Mead simplex methods, 71 Levy flight mechanism, 72 Brownian random w...…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis and validation of intelligent tool based on Brownian random walk‐based sand cat swarm optimization algorithm for parameter identification of various solar photovoltaic mathematical models

Raja¹,

Kumar

Sivaraju

et al. 2023

Int J Numerical Modelling

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The photovoltaic (PV) system stands out as a viable energy source due to its environmental friendliness and cleanliness. The conversion rate at which solar power generation is still relatively low due to limitations imposed by advances in PV technology. For PV systems, an appropriate model with precise internal parameters is considerably more crucial to increase conversion efficiency further. Different PV mathematical models, such as single‐diode, two‐diode, and three‐diode, are available to model the PV system. Investigators are interested in assessing the accurate PV model parameters through the experimental voltage–current (I–V) samples or using the manufacturer's specifications. At the same time, the difficulty is in accurately assessing and developing a more trustworthy PV model with well‐optimized parameters. To address the parameter estimation of various solar PV models, in this article, a new bio‐inspired algorithm called Brownian random walk‐based Sand Cat Swarm Optimization Algorithm (SCSOA) named Boosted SCSOA (BSCSOA) is proposed and developed. Along with the Brownian random strategy, chaotic tent drift is also used to enhance the exploration and exploitation of SCSOA, and the proposed BSCSOA is applied to different models to estimate their parameters accurately. The effectiveness of the suggested BSCSOA is compared with other well‐known algorithms, including the basic SCSOA, in terms of statistical measures and fitness values. The obtained results demonstrated the superiority of the BSCSOA over the other algorithms for all PV models of the cell and module.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance analysis and validation of intelligent tool based on Brownian random walk‐based sand cat swarm optimization algorithm for parameter identification of various solar photovoltaic mathematical models

Raja¹,

Kumar

Sivaraju

et al. 2023

Int J Numerical Modelling

View full text Add to dashboard Cite

show abstract

“…The results proved the ability of CGBO dealing with this problem, where it obtained the highest accuracy solutions of PV parameters with lowest run time compared state-of-the-art algorithms. Also in [85], the authors solved solar photovoltaic model. But this time, orthogonal learning (OL) was used with GBO (RLGBO) to enhance its speed rate.…”

Section: Energy and Power Flowmentioning

confidence: 99%

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Daoud

Shehab

Al-Mimi

et al. 2022

Arch Computat Methods Eng

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This paper introduces a comprehensive survey of a new population-based algorithm so-called gradient-based optimizer (GBO) and analyzes its major features. GBO considers as one of the most effective optimization algorithm where it was utilized in different problems and domains, successfully. This review introduces set of related works of GBO where distributed into; GBO variants, GBO applications, and evaluate the efficiency of GBO compared with other metaheuristic algorithms. Finally, the conclusions concentrate on the existing work on GBO, showing its disadvantages, and propose future works. The review paper will be helpful for the researchers and practitioners of GBO belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.

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“…The GSR strategy has shown great capability in many tasks [89][90][91][92][93]. In this study, the GSR strategy is mixed with the position update of the CSA to enhance the global search capability.…”

Section: Gradient Search Rule and Local Escaping Operator And Levy Fl...mentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

Self Cite

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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Solar photovoltaic model parameter estimation based on orthogonally-adapted gradient-based optimization

Cited by 33 publications

References 112 publications

Performance analysis and validation of intelligent tool based on Brownian random walk‐based sand cat swarm optimization algorithm for parameter identification of various solar photovoltaic mathematical models

Performance analysis and validation of intelligent tool based on Brownian random walk‐based sand cat swarm optimization algorithm for parameter identification of various solar photovoltaic mathematical models

Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

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