Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system
Top Cited Papers
Open Access
- 6 February 2021
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Science, Measurement & Technology
- Vol. 15 (2), 218-231
- https://doi.org/10.1049/smt2.12023
Abstract
The transmission line is the main component in the power system consisting of inductance, capacitance, and resistance. These parameters are important during the transmission line design. This research work applies a novel optimisation technique, grey wolf optimisation (GWO), to calculate the overhead transmission line parameter. The best optimal value is estimated with the control variables. Furthermore, the effect of different bundle conductors, that is, two, three, and four bundle conductors, radius, and spacing between the conductors on the transmission line is also analysed. GWO is a recently developed nature‐inspired meta‐heuristic algorithm. Single‐phase and three‐phase transmission line test systems have been adopted for testing purposes. The proposed algorithm is inspired by the command hierarchy and hunting system of grey wolves. The algorithm is applied to 14 benchmark optimisation functions with dimension and number of search agents. The results of the GWO algorithms are optimised and are superior as compared to previously applied algorithms. The proposed algorithm achieved the best optimal solutions for most of these functions that have been validated statistically. From the results, it is identified that the proposed algorithm is computationally efficient and performs significantly better in terms of accuracy, robustness, and convergence speed.Keywords
This publication has 71 references indexed in Scilit:
- Discrete cuckoo search algorithm for the travelling salesman problemNeural Computing & Applications, 2013
- Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulationsBehavioural Processes, 2011
- An improved particle swarm optimization for feature selectionJournal of Bionic Engineering, 2011
- A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimizationEuropean Journal of Computational Mechanics, 2008
- Efficiently Solving the Redundancy Allocation Problem Using Tabu SearchIIE Transactions, 2003
- Feature selection using tabu search methodPattern Recognition, 2002
- Alpha status, dominance, and division of labor in wolf packsCanadian Journal of Zoology, 1999
- No free lunch theorems for optimizationIEEE Transactions on Evolutionary Computation, 1997
- Multi-objective genetic algorithm and its applications to flowshop schedulingComputers & Industrial Engineering, 1996
- Improving the Efficiency of Tabu Search for Machine Sequencing ProblemsJournal of the Operational Research Society, 1993