A modified grey wolf optimizer for improving wind plant energy production
The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This pap...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-06-01.
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| LEADER | 02441 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs21529_13733 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Tumari, Mohd Zaidi Mohd |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Suid, Mohd Helmi |e author |
| 700 | 1 | 0 | |a Ahmad, Mohd Ashraf |e author |
| 245 | 0 | 0 | |a A modified grey wolf optimizer for improving wind plant energy production |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21529 | ||
| 520 | |a The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Wind farm; Wake interactions; Energy production; Metaheuristic algorithm; Nature inspired algorithm | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 3: June 2020; 1123-1129 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21529/13733 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21529/13733 |z Get fulltext |