Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Yu, Sudan; Chen, Zhiqing; Heidari, Ali Asghar; Zhou, Wei; Chen, Huiling; Xiao, Lei

doi:10.1049/rpg2.12451

Cited by 27 publications

(13 citation statements)

References 127 publications

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“…Renewable energy [65] LSSVM-GBO Using LSSVM-GBO to estimate the parameters of the water quality Water quality [66] GBO-MFO Using GBO-MFO to determine the best power flow based on the optimal position and the volume of the wind energy…”

Section: Sdgbomentioning

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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Section: Sdgbomentioning

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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“…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

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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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“…An improved GBO with chaotic maps and self-adaptive weighting has been used to estimate the optimal parameters of the PV generating unit (Jiang et al, 2022). A hybrid sine-cosine algorithm (SCA) with differential GBO was introduced to estimate the PV parameters, whereas the differential evolution (DE) and SCA are used to avoid being stuck in local optima and get the global solution (Yu et al, 2022). An approach of hunger games search optimizer (HGSO) with mutation of Cauchy-and Gaussian-based improved Newton-Raphson method has been applied to build and analyze the TDM circuit of the PV panel (Premkumar et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An efficient capuchin search algorithm for extracting the parameters of different PV cells/modules

et al. 2022

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Constructing an equivalent circuit for the photovoltaic (PV) generating unit converging the real operation is a difficult process because of unavailability of some parameters. Many approaches have been conducted in this field; however, they have some problems in computational time and are stuck in local optima. Therefore, this study proposes a simple, robust, and efficient methodology-incorporated capuchin search algorithm (CapSA) to construct the equivalent circuit of the PV generating unit via identifying its parameters. The CapSA is selected as it is simple and requires less computational time in addition to exploration/exploitation balance that avoids local optima. The process is formulated as an optimization problem, which aims at minimizing the root mean square error (RMSE) between measured and simulated currents. A single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) of different PV cells and panels operating at either constant or variable weather conditions are constructed. A comparison to different programmed metaheuristic approaches is conducted. The best RMSE values obtained by the proposed CapSA are 2.27804E-04, 1.3808E-04, and 1.5182E-04 for SDM, DDM, and TDM of PVW 752 cell, respectively. For the KC200GT panel, the proposed approach achieved the best fitness values of 3.4440E-04, 1.5617E-03, and 6.6008E-03 at 25°C, 50°C, and 75°C, respectively. The obtained results confirmed the superiority and competence of the proposed CapSA in constructing a reliable equivalent circuit for the PV cell/panel.

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Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Cited by 27 publications

References 127 publications

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

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

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

An efficient capuchin search algorithm for extracting the parameters of different PV cells/modules

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