scholarly journals An Effective Method for Parameter Estimation of Solar PV Cell Using Grey-Wolf Optimization Technique

2021 ◽  
Vol 6 (3) ◽  
pp. 911-931
Author(s):  
Abhishek Sharma ◽  
Abhinav Sharma ◽  
Averbukh Moshe ◽  
Nikhil Raj ◽  
Rupendra Kumar Pachauri
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Parameter Extraction ◽  
Solar Pv ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Pv Cell ◽  
Gravitational Search

In the field of renewable energy, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction of solar PV cell is a highly non-linear complex optimization problem. In this research work, the authors have explored grey wolf optimization (GWO) algorithm to estimate the optimized value of the unknown parameters of a PV cell. The simulation results have been compared with five different pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), chicken swarm optimization (CSO) and cultural algorithm (CA). Furthermore, a comparison with the algorithms existing in the literature is also carried out. The comparative results comprehensively demonstrate that GWO outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and the rate of convergence. Furthermore, the statistical results validate and indicate that GWO algorithm is better than other algorithms in terms of average accuracy and robustness. An extensive comparison of electrical performance parameters: maximum current, voltage, power, and fill factor (FF) has been carried out for both PV model.

scholarly journals Parameter Extraction of Photovoltaic Module Using Tunicate Swarm Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 878
Author(s):  
Abhishek Sharma ◽  
Ankit Dasgotra ◽  
Sunil Kumar Tiwari ◽  
Abhinav Sharma ◽  
Vibhu Jately ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Research Work ◽  
Parameter Extraction ◽  
Photovoltaic Module ◽  
Solar Pv ◽  
Whale Optimization ◽  
Gravitational Search ◽  
Swarm Algorithm

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

Fusion of Gravitational Search Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer for Odor Source Localization

2018 ◽  
pp. 276-302
Author(s):  
Upma Jain ◽  
Ritu Tiwari ◽  
W. Wilfred Godfrey
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robots ◽  
Source Localization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Grey Wolf Optimizer ◽  
Odor Source ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Gravitational Search

This chapter concerns the problem of odor source localization by a team of mobile robots. A brief overview of odor source localization is given which is followed by related work. Three methods are proposed for odor source localization. These methods are largely inspired by gravitational search algorithm, grey wolf optimizer, and particle swarm optimization. Objective of the proposed approaches is to reduce the time required to localize the odor source by a team of mobile robots. The intensity of odor across the plume area is assumed to follow the Gaussian distribution. Robots start search from the corner of the workspace. As robots enter in the vicinity of plume area, they form groups using K-nearest neighbor algorithm. To avoid stagnation of the robots at local optima, search counter concept is used. Proposed approaches are tested and validated through simulation.

scholarly journals Ajuste Ótimo de ESP e SVC-POD para Amortecimento de Oscilações

2021 ◽  
Author(s):  
Wesley Peres ◽  
Bruna C. Ferreira ◽  
Fabrício C. Gonçalves ◽  
Felipe L. S. Magalhães ◽  
Junior N. N. Costa ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Power Oscillation ◽  
Whale Optimization ◽  
Gravitational Search

O amortecimento de oscilações de potência é essencial na operação de sistemas de potência. Oscilações não amortecidas ou fracamente amortecidas podem limitar a capacidade de transferência de potência e causar blecautes. Para resolver esse problema, estabilizadores de sistemas de potência (ESP) instalados em geradores síncronos têm sido utilizados desde a década de setenta. Outra opção é utilizar um controlador denominado Power Oscillation Damper (POD) em dispositivos FACTS tais como o Compensador Estático de Reativos (CER). Com o objetivo de melhorar a estabilidade dos sistemas de potência, um projeto ótimo e robusto de ESP e POD deve ser realizado. Considerado as soluções de boa qualidade fornecidas por metaheurísticas, esse artigo compara quatro técnicas (Whale Optimization Algorithm, Grey Wolf Optimization, Gravitational Search Algorithm e Algoritmos Genéticos) na solução do problema de otimização mencionado. O ajuste de controladores ESP e POD é formulado como um problema de otimização com o objetivo de maximizar o coeficiente de amortecimento do autovalor dominante em malha fechada considerando vários pontos de operação para garantia de robustez. Resultados para um sistema de duas áreas são discutidos.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

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