scholarly journals Study of a New Hybrid Optimization-Based Method for Obtaining Parameter Values of Solar Cells

2020 ◽  
Author(s):  
Selma Tchoketch_Kebir
Keyword(s):  
Solar Cells ◽  
Hybrid Method ◽  
Traditional Method ◽  
Hybrid Methods ◽  
Optimization Method ◽  
Parameter Determination ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unknown Parameters ◽  
Parameter Values

This chapter presents a comprehensive study of a new hybrid method developed for obtaining the electrical unknown parameters of solar cells. The combination of a traditional method and a recent smart swarm-based optimization method is done, with a big focus on the application of the topic of artificial intelligence algorithms into solar photovoltaic production. The combined approach was done between the traditional method, which is the noniterative Levenberg-Marquardt technic and between the recent meta-heuristic optimization technic, called Grey Wolf optimizer algorithm. For comparison purposes, some other classical solar cell parameter determination optimization-based methods are carried out, such as the numerical (iterative, noniterative) methods, the meta-heuristics (evolution, human, physic, and swarm) methods, and other hybrid methods. The final obtained results show that the used hybrid method outperforms the above-mentioned classical methods, under this study.

scholarly journals A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2147 ◽  
Author(s):  
Zhihang Yue ◽  
Sen Zhang ◽  
Wendong Xiao
Keyword(s):  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Grey Wolf Optimizer ◽  
Local Optimum ◽  
Grey Wolf ◽  
Optimal Search ◽  
Fireworks Algorithm ◽  
Wide Range ◽  
Search Capability

Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.

scholarly journals Developing Software of Global Optimization Method Based on Grey Wolf Optimizer

2021 ◽  
Vol 11 (2) ◽  
pp. 59-73
Author(s):  
A.V. Panteleev ◽  
I.A. Belyakov
Keyword(s):  
Optimization Method ◽  
Step Function ◽  
Algorithm Analysis ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Test Functions ◽  
Global Extremum ◽  
The Cost ◽  
Bioinspired Algorithms ◽  
Selection Of

This article discusses the development of software that allows to simulate the algorithm of the “Grey Wolf Optimizer” method. This algorithm belongs to the class of metaheuristic algorithms that allow finding a global extremum on a set of admissible solutions. This algorithm is being the most efficiently used in a situation where the cost function is specified in the form of a black box. The algorithm belongs to both bioinspired algorithms and to the class of algorithms of Particle Swarm Optimization. To analyze the efficiency of the algorithm, software was created that allows to vary the parameters of the method. The article contains examples of the program’s work on various test functions. The purpose of the program is to collect and analyze statistical results, making possible to evaluate the final result. The program provides to build graphs that make it possible to make a more thorough assessment of the results obtained. The program has a step-by-step function that allows one to analyze the specifics and features of the algorithm. Analysis of statistical data provides more detailed selection of the parameters of the algorithm.

scholarly journals An Improved Artificial Jellyfish Search Optimizer for Parameter Identification of Photovoltaic Models

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1867
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan ◽  
Attia El-Fergany
Keyword(s):  
Thin Film ◽  
Solar Cells ◽  
Convergence Rate ◽  
Numerical Simulations ◽  
Solar Pv ◽  
Unknown Parameters ◽  
Pv Systems ◽  
Operation Conditions ◽  
Pv Modules ◽  
Parameter Values

The optimization of photovoltaic (PV) systems relies on the development of an accurate model of the parameter values for the solar/PV generating units. This work proposes a modified artificial jellyfish search optimizer (MJSO) with a novel premature convergence strategy (PCS) to define effectively the unknown parameters of PV systems. The PCS works on preserving the diversity among the members of the population while accelerating the convergence toward the best solution based on two motions: (i) moving the current solution between two particles selected randomly from the population, and (ii) searching for better solutions between the best-so-far one and a random one from the population. To confirm its efficacy, the proposed method is validated on three different PV technologies and is being compared with some of the latest competitive computational frameworks. The numerical simulations and results confirm the dominance of the proposed algorithm in terms of the accuracy of the final results and convergence rate. In addition, to assess the performance of the proposed approach under different operation conditions for the solar cells, two additional PV modules (multi-crystalline and thin-film) are investigated, and the demonstrated scenarios highlight the utility of the proposed MJSO-based methodology.

Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm

2016 ◽  
Vol 6 (3) ◽  
pp. 1353 ◽  
Author(s):  
Yannis L Karnavas ◽  
Ioannis D Chasiotis ◽  
Emmanouil L Peponakis
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnets ◽  
Permanent Magnet Synchronous Motor ◽  
Optimization Method ◽  
Synchronous Motor ◽  
Optimization Techniques ◽  
Attractive Alternative ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Low Speed

Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “<em>Grey Wolf Optimizer</em>” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques.

scholarly journals On the application of nature-inspired grey wolf optimizer algorithm in geodesy

2020 ◽  
Vol 10 (1) ◽  
pp. 48-52
Author(s):  
M. Yetkin ◽  
O. Bilginer
Keyword(s):  
Optimization Problems ◽  
Optimization Method ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Absolute Value ◽  
Robust Parameter ◽  
First Time ◽  
Electronic Distance Measurement

AbstractNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.

scholarly journals Identification of Parameters of Richards Equation Using Modified Hybrid Grey Wolf Optimizer-Genetic Algorithm (HmGWOGA)

2019 ◽  
Vol 12 (4) ◽  
pp. 1567-1583
Author(s):  
Wenddabo Olivier Sawadogo ◽  
Pengdwende Ousseni Fabrice Ouedraogo ◽  
Ousseni So ◽  
Genevieve Barro ◽  
Blaise Some
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Meshless Method ◽  
Direct Problem ◽  
Pressure Head ◽  
Optimization Method ◽  
Richards Equation ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Better Than

In this paper, it is a question of identification of the parameters in the equation ofRichards modelling the flow in unsaturated porous medium. The mixed formulation pressure head-moisture content has been used. The direct problem was solved using Multiquadratic Radial Basis Function ( RBF-MQ ) method which is a meshless method. The Newton-Raphson’s method was used to linearize the equation. The function cost used is built by using the infiltration. The optimization method used is a meta-heuristic called Modified hybrid Grey Wolf Optimizer -Genetic Algorithm (HmGWOGA). A test on experimental data has been carried. We compared the results with genetic algorithms. The results showed that this new method was better than genetic algorithms.

scholarly journals Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm

2016 ◽  
Vol 6 (3) ◽  
pp. 1353 ◽  
Author(s):  
Yannis L Karnavas ◽  
Ioannis D Chasiotis ◽  
Emmanouil L Peponakis
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnets ◽  
Permanent Magnet Synchronous Motor ◽  
Optimization Method ◽  
Synchronous Motor ◽  
Optimization Techniques ◽  
Attractive Alternative ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Low Speed

Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “<em>Grey Wolf Optimizer</em>” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques.

scholarly journals Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Sen Zhang ◽  
Yongquan Zhou
Keyword(s):  
Data Clustering ◽  
Optimization Problems ◽  
Optimization Method ◽  
Local Optimization ◽  
Superior Performance ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Grey Wolf ◽  
Data Mining Technique ◽  
Local Optimization Method

One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Grey wolf optimizer algorithm for the performance predetermination of variable speed self-excited induction generators

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Essaki Raj R. ◽  
Sundaramoorthy Sridhar
Keyword(s):  
Objective Function ◽  
Core Loss ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Unknown Parameters ◽  
Content Type ◽  
Induction Generators ◽  
Loss Resistance ◽  
The Given

Purpose This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance.

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