.png){kind=icon}
.png){kind=icon}
Return
Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2019.105937.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes an enhancement of the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). Then the statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). Also, the non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA. After that, the EWOA and WOA are implemented to design optimal Takagi-Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Moreover, real wind speed data has confirmed the robustness of optimal EWOA-MPPT. In conclusion, the simulation results revealed that the EWOA is a promising algorithm to be applied for solving different engineering problems. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Benchmark functions
Optimization methods
Whale optimization algorithm
Wind generator
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W