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.
Highlights
Quality of life improvements are necessary as an economy and society develop
The meta-heuristic optimization algorithms are used to extract the optimum solution for several problems. One of these problems is the estimation of parameters in photovoltaic models such as the Harris Hawks optimization [9], the Marine Predators Algorithm [10], the multi-strategy success-history-based adaptive differential evolution [11], the bacterial foraging algorithm [12], the differential evolution algorithms [13], Enhanced leader particle swarm optimization (ELPSO) [14], Time varying acceleration coefficients particle swarm optimization (TVACPSO) [15], and the shuffled frog leaping algorithm [16]
Tab. 4 show the best solution from the proposed Gradient-Based Optimizer (GBO) algorithm in compared with Tunicate swarm algorithm (TSA) [29] and Chimp optimization algorithm (ChOA) [30] for 6-kW wind turbine
Summary
Quality of life improvements are necessary as an economy and society develop. One corresponding challenge is to decrease environmental pollution. Replacing fossil fuels with clean energy is one of the main components to decrease environmental pollution. Industries and academic institutions alike are interested in developing electricity from renewable and clean energy sources, and with. When wind hits the wind cube, it concentrates and generates the speed, in turn producing more power. The modern wind cube system has been designed to solve low wind speed problems and collect wind power under these circumstances [8]. The meta-heuristic optimization algorithms are used to extract the optimum solution for several problems. Metaheuristic optimization applied to a wind farm layout is one of the main tools used to determine optimum wind farm position and maximize the generated power. The paper organization is as follows, Section two explains the problem formulation and metrological data.
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