scholarly journals Orthogonal Learning-Based Gray Wolf Optimizer for Identifying the Uncertain Parameters of Various Photovoltaic Models

Optik ◽  
2021 ◽  
pp. 167973
Author(s):  
Felix Joseph Xavier ◽  
A. Pradeep ◽  
M. Premkumar ◽  
C. Kumar
Keyword(s):  
Gray Wolf ◽  
Uncertain Parameters ◽  
Gray Wolf Optimizer ◽  
Orthogonal Learning

Orthogonal Learning-Based Gray Wolf Optimizer For Identifying The Uncertain Parameters of Various Photovoltaic Models

2021 ◽  
Author(s):  
Felix Joseph Xavier ◽  
A.Pradeep ◽  
A.Anbarasan ◽  
C.Kumar
Keyword(s):  
Identification Problem ◽  
Photovoltaic System ◽  
Computational Techniques ◽  
Gray Wolf ◽  
Unknown Parameters ◽  
Pv System ◽  
Gray Wolf Optimizer ◽  
Parameter Identification Problem ◽  
Exploitation And Exploration ◽  
Orthogonal Learning

Abstract Determining the optimal parameters for the photovoltaic system (PV) model is essential during the design, evolution, development, estimation, and PV systems analysis. Therefore, it is crucial for the proper advancement of the best parameters of the PV models based on modern computational techniques. Thus, this work suggests a new Orthogonal-Learning-Based Gray Wolf Optimizer (OLBGWO) through a local exploration for estimating the unknown variables of PV cell models. The exploitation and exploration capability of the basic Gray Wolf Optimizer (GWO) is improved by the orthogonal-learning-based (OLB) approach, and this arrangement promotes a highly reliable equilibrium between the exploitation and exploration levels of the algorithm. In OLBGWO, the OLB strategy is used to find the best solution for the poor populations and directs the population to review the potential search area during the iterative process. Also, an exponential decay function is employed to decrease the value of vector a in GWO. The developed algorithm is directly applied to the parameter identification problem of the PV system. The proposed OLBGWO algorithm estimates the unknown parameters of the single-diode model (SDM), double-diode model (DDM), and PV module model. The performance of the OLBGWO is compared with other competitive algorithms to prove its superiority. The simulation results prove that the OLBGWO algorithm can achieve high solution accuracy with high convergence speed.

scholarly journals An Improved Gray Wolf Optimizer MPPT Algorithm for PV System With BFBIC Converter Under Partial Shading

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 103476-103490 ◽  
Author(s):  
Ke Guo ◽  
Lichuang Cui ◽  
Mingxuan Mao ◽  
Lin Zhou ◽  
Qianjin Zhang
Keyword(s):  
Gray Wolf ◽  
Pv System ◽  
Partial Shading ◽  
Gray Wolf Optimizer

Using the gray wolf optimizer for solving optimal reactive power dispatch problem

2015 ◽  
Vol 32 ◽  
pp. 286-292 ◽  
Author(s):  
Mohd Herwan Sulaiman ◽  
Zuriani Mustaffa ◽  
Mohd Rusllim Mohamed ◽  
Omar Aliman
Keyword(s):  
Reactive Power ◽  
Gray Wolf ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Gray Wolf Optimizer ◽  
Reactive Power Dispatch

scholarly journals A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

2021 ◽  
Vol 14 (1) ◽  
pp. 296
Author(s):  
Mohanad A. Deif ◽  
Ahmed A. A. Solyman ◽  
Mohammed H. Alsharif ◽  
Seungwon Jung ◽  
Eenjun Hwang
Keyword(s):  
Support Vector Machine ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Support Vector ◽  
Gray Wolf ◽  
Particle Swarm Optimizer ◽  
Numerical Weather ◽  
Air Temperatures ◽  
Gray Wolf Optimizer ◽  
Accuracy And Stability

Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.

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