Performance of Gradient-Based Optimizer for Optimum Wind Cube Design
Source abstract
Abstract
Renewable energy is a safe and limitless energy source that can be utilized for heating, cooling, and other purposes. Wind energy is one of the most important renewable energy sources. Power fluctuation of wind turbines occurs due to variation of wind velocity. A wind cube is used to decrease power fluctuation and increase the wind turbine’s power. The optimum design for a wind cube is the main contribution of this work. The decisive design parameters used to optimize the wind cube are its inner and outer radius, the roughness factor, and the height of the wind turbine hub. A Gradient-Based Optimizer (GBO) is used as a new metaheuristic algorithm in this problem. The objective function of this research includes two parts: the first part is to minimize the probability of generated energy loss, and the second is to minimize the cost of the wind turbine and wind cube. The Gradient-Based Optimizer (GBO) is applied to optimize the variables of two wind turbine types and the design of the wind cube. The metrological data of the Red Sea governorate of Egypt is used as a case study for this analysis. Based on the results, the optimum design of a wind cube is achieved, and an improvement in energy produced from the wind turbine with a wind cube will be compared with energy generated without a wind cube. The energy generated from a wind turbine with the optimized cube is more than 20 times that of a wind turbine without a wind cube for all cases studied.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Wind Energy Research and Development · Advanced Multi-Objective Optimization Algorithms · Solar Radiation and Photovoltaics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (30)
Show source-provided references
1. Optimized sizing of a standalone pv-wind-hydropower station with pumped-storage installation hybrid energy system10.1016/j.renene.2019.09.099 ↗
2. Design, simulation and experimental evaluation of energy system for an unmanned surface vehicle10.1016/j.energy.2018.01.158 ↗
3. Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system10.1016/j.apenergy.2020.115052 ↗
4. Present situation and future prospect of renewable energy in china10.1016/j.rser.2017.03.023 ↗
5. Should china focus on the distributed development of wind and solar photovoltaic power generation? A comparative study10.1016/j.apenergy.2016.11.004 ↗
6. A novel review on optimization techniques used in wind farm modelling10.1016/j.ref.2020.09.001 ↗
7. Design and implementation of the 10 kw wind cube
8. The 30 kva permanent magnet synchronous generator wind-cube design, implementation, control; and performance measurements and analysis
9. Parameters extraction of three-diode photovoltaic model using computation and harris hawks optimization10.1016/j.energy.2020.117040 ↗
10. Marine predators algorithm for parameters identification of triple-diode photovoltaic models10.1109/ACCESS.2020.3019244 ↗
11. Parameters identification of photovoltaic models using a multi-strategy success-history-based adaptive differential evolution10.1109/ACCESS.2020.2975078 ↗
12. Bacterial foraging optimization approach to parameter extraction of a photovoltaic module10.1109/TSTE.2017.2736060 ↗
13. An accurate modelling of the two-diode model of pv module using a hybrid solution based on differential evolution10.1016/j.enconman.2016.06.076 ↗
14. Enhanced leader particle swarm optimisation (ELPSO): An efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules10.1016/j.solener.2017.10.063 ↗
15. Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules10.1016/j.enconman.2016.09.085 ↗
16. Shuffled frog leaping algorithm for photovoltaic model identification10.1109/TSTE.2015.2389858 ↗
17. Optimization of wind farm layout with modified genetic algorithm based on boolean code10.1016/j.jweia.2018.07.019 ↗
18. Comparative study of discretization method and monte carlo method for wind farm layout optimization under weibull distribution10.1016/j.jweia.2018.07.021 ↗
19. Optimization of wind turbines siting in a wind farm using genetic algorithm based local search10.1016/j.renene.2018.02.083 ↗
20. Optimization of wind turbine placement in a wind farm using a new pseudo-random number generation method10.1016/j.renene.2018.02.082 ↗
21. An advanced modeling system for optimization of wind farm layout and wind turbine sizing using a multi-level extended pattern search algorithm10.1016/j.energy.2015.12.033 ↗
22. Controller energy management for hybrid renewable energy system
23. Operation study of a combined system of diesel engine generators and wind turbine with battery storage system
24. An optimal sizing algorithm for a hybrid renewable energy system
25. Designing of stand-alone hybrid pv/wind/battery system using improved crow search algorithm considering reliability index10.1007/s40095-019-00319-y ↗
26. Gradient-based optimizer: A new metaheuristic optimization algorithm10.1016/j.ins.2020.06.037 ↗
27. Gradient-based optimizer for parameter extraction in photovoltaic models10.1109/ACCESS.2021.3052153 ↗
28. Recent methodology-based gradient-based optimizer for economic load dispatch problem10.1109/ACCESS.2021.3066329 ↗
29. Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization10.1016/j.engappai.2020.103541 ↗
30. Chimp optimization algorithm10.1016/j.eswa.2020.113338 ↗