基于K-means-GA的海上风电场检查路径的优化
Zhongbo Peng1, Shijie Sun1, Liang Tong1
1School of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing, China.
PloS one
|May 23, 2024
概括
这项研究优化了使用K-means集群和遗传算法 (GA) 的海上风电场检查. 综合方法显著减少了检查路线和运营成本,提高了风能的利能力.
科学领域:
- 可再生能源工程可再生能源工程
- 运营研究 运营研究
- 计算优化计算优化
背景情况:
- 全球对海上风能需求的不断增长需要降低成本和提高利能力.
- 有效的运营策略对于海上风电场的经济可行性至关重要.
- 目前的检查路线规划方法可能无法完全解决大规模海上作业的复杂性.
研究的目的:
- 开发和评估一个综合方法,以优化海上风电场检查路线.
- 为了降低运营成本,提高检查物流的效率.
- 将检查路线规划制定为多个旅行销售员问题 (mTSP).
主要方法:
- 整合K-means集群算法用于初始分组风力轮机位置.
- 应用遗传算法 (GA) 来改进检查路线并降低成本.
- 将检查路线规划问题作为多个旅行销售员问题 (mTSP) 的表述.
主要成果:
- 综合的K-means和GA方法显著减少了检查路线的长度,从93公里减少到79.36公里.
- 运营成本显著下降,从141,500元降至125,600元.
- 模拟实验证实了拟议的综合方法对传统GA的有效性和优越性.
结论:
- 结合K-means集群和遗传算法方法,为优化海上风电场检查路线提供了有效的解决方案.
- 这种方法导致路线长度和运营支出大幅减少.
- 这些发现支持采用先进的计算优化技术来提高可再生能源基础设施的经济性能.
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