Abstract

Energy issues have always been one of the most significant concerns for scientists worldwide. With the ongoing over exploitation and continued outbreaks of wars, traditional energy sources face the threat of depletion. Wind energy is a readily available and sustainable energy source. Wind farm layout optimization problem, through scientifically arranging wind turbines, significantly enhances the efficiency of harnessing wind energy. Meta-heuristic algorithms have been widely employed in wind farm layout optimization. This paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm, referred to as AIGA, for optimizing wind farm layout problems. The adaptive strategy dynamically adjusts the placement of wind turbines, leading to a substantial improvement in energy utilization efficiency within the wind farm. In this study, AIGA is tested in four different wind conditions, alongside four other classical algorithms, to assess their energy conversion efficiency within the wind farm. Experimental results demonstrate a notable advantage of AIGA.

中文翻译：

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一种自适应策略的风电场布局优化整数遗传算法

摘要

能源问题一直是全世界科学家最关心的问题之一。随着过度开采和战争持续爆发，传统能源面临枯竭的威胁。风能是一种容易获得且可持续的能源。风电场布局优化问题，通过科学布置风电机组，显着提高风能利用效率。元启发式算法已广泛应用于风电场布局优化。本文介绍了一种结合自适应策略的整数遗传算法（AIGA）来优化风电场布局问题。自适应策略动态调整风力发电机的放置位置，从而大幅提高风电场内的能源利用效率。在这项研究中，AIGA 与其他四种经典算法一起在四种不同的风力条件下进行了测试，以评估其在风电场内的能量转换效率。实验结果表明AIGA具有显着的优势。