An advanced Grey Wolf Optimization Algorithm and its application to planning problem in smart grids

Autor: Bahman Ahmadi, Soheil Younesi, Oguzhan Ceylan, Aydogan Ozdemir
Přispěvatelé: Ahmadi B., Younesi S., Ceylan O., Özdemir A.
Rok vydání: 2022
Předmět:
COMPUTER SCIENCE
ARTIFICIAL INTELLIGENCE
General Computer Science
BİLGİSAYAR BİLİMİ
İNTERDİSİPLİNER UYGULAMALAR
Evolutionary computation
algorithms
DISTRIBUTION-SYSTEMS
Theoretical Computer Science
BİLGİSAYAR BİLİMİ
YAPAY ZEKA
Smart grid applications
REANALYSIS
CAPACITORS
Artificial Intelligence
Computer Graphics
Computer Science (miscellaneous)
BAT ALGORITHM
Bilgisayar Bilimleri
OPTIMAL ALLOCATION
Computers in Earth Sciences
Engineering
Computing & Technology (ENG)
Computer Sciences
OPTIMAL PLACEMENT
DISTRIBUTED GENERATION
Mühendislik
Bilişim ve Teknoloji (ENG)
COMPUTER SCIENCE
INTERDISCIPLINARY APPLICATIONS
COMPUTER SCIENCE
Bilgisayar Grafiği
Computer Graphics and Computer-Aided Design
Computer Science Applications
Optimization algorithm
Renewable energy integration
Physical Sciences
Engineering and Technology
Bilgisayar Bilimi
Mühendislik ve Teknoloji
Computer Vision and Pattern Recognition
Geometry and Topology
Algoritmalar
Software
Zdroj: Soft Computing. 26:3789-3808
ISSN: 1433-7479
1432-7643
Popis: Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.
Databáze: OpenAIRE
Externí odkaz: