基于Voronoi图的电动汽车与主动配电网电力系统规划中的多重追捕者算法复现与实现,基于Voronoi图的电动汽车与主动配电网电力系统规划中的多追捕者围捕算法matlab复现指南,电动汽车，主动配电网
资源内容介绍
基于Voronoi图的电动汽车与主动配电网电力系统规划中的多重追捕者算法复现与实现,基于Voronoi图的电动汽车与主动配电网电力系统规划中的多追捕者围捕算法matlab复现指南,电动汽车，主动配电网，电力系统规划对lunwen《Intercepting Rogue Robots: An Algorithm for Capturing Multiple Evaders With Multiple Pursuers》的matlab复现，基于voronoi图维诺图最小化围捕算法 完整注释,电动汽车; 主动配电网; 电力系统规划; 算法复现; Voronoi图; 围捕算法; 最小化围捕,MATLAB复现：基于Voronoi图的最小化围捕算法在电动汽车与主动配电网的电力系统规划中的应用 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90431825/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90431825/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">抱歉，<span class="_ _0"></span>我不能直接写一篇完全随机的文章，<span class="_ _0"></span>因为它不遵循任何特定的结构或主题。<span class="_ _0"></span>但我可以</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">为您提供一个围绕您所给关键词的文章构想。<span class="_ _1"></span>下面是一篇示例文章，<span class="_ _1"></span>您可根据需要进行修改</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">和扩展。</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">**<span class="ff1">基于<span class="_ _2"> </span></span>Voronoi<span class="_"> </span><span class="ff1">图维诺图最小化围捕算法的电动汽车主动配电网电力系统规划</span>**</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">随<span class="_ _3"></span>着<span class="_ _3"></span>科<span class="_ _3"></span>技<span class="_ _3"></span>的<span class="_ _3"></span>发<span class="_ _3"></span>展<span class="_ _3"></span>，<span class="_ _3"></span>电<span class="_ _3"></span>动<span class="_ _3"></span>汽<span class="_ _3"></span>车<span class="_ _3"></span>（<span class="_ _3"></span><span class="ff2">Electric <span class="_ _3"></span>Vehicles, <span class="_ _3"></span>EVs<span class="_ _3"></span></span>）<span class="_ _3"></span>日<span class="_ _3"></span>益<span class="_ _3"></span>普<span class="_ _3"></span>及<span class="_ _3"></span>，<span class="_ _3"></span>对<span class="_ _3"></span>于<span class="_ _3"></span>主<span class="_ _3"></span>动<span class="_ _3"></span>配<span class="_ _3"></span>电<span class="_ _3"></span>网<span class="_ _3"></span>（<span class="_ _3"></span><span class="ff2">Active </span></div><div class="t m0 x1 h2 y7 ff2 fs0 fc0 sc0 ls0 ws0">Distribution Networks, ADNs<span class="_ _3"></span><span class="ff1">）的管理<span class="_ _3"></span>与规划显<span class="_ _3"></span>得愈发重<span class="_ _3"></span>要。在<span class="_ _3"></span>此背景下<span class="_ _3"></span>，电力系<span class="_ _3"></span>统规划技</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">术正在<span class="_ _3"></span>寻求创新性<span class="_ _3"></span>的方法来解<span class="_ _3"></span>决电动汽车<span class="_ _3"></span>大规模接入<span class="_ _3"></span>带来的问题<span class="_ _3"></span>。本文旨<span class="_ _3"></span>在提出一种<span class="_ _3"></span>基于</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">Voronoi<span class="_"> </span><span class="ff1">图维诺图最小化围捕算法（</span>Voronoi-Voronoi-based <span class="_ _4"></span>Minimum <span class="_ _4"></span>Enclosure Algorithm, </div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">VMBMEA<span class="ff1">）的优化策略，以提高电力系统的稳定性和效率。</span></div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">二、算法简介</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _5"> </span><span class="ff2">Intercepting <span class="_ _3"></span>Rogue Robots<span class="_"> </span></span>的<span class="_ _3"></span>算<span class="_ _3"></span>法研<span class="_ _3"></span>究<span class="_ _3"></span>中<span class="_ _3"></span>，存<span class="_ _3"></span>在<span class="_ _3"></span>一种<span class="_ _3"></span>基<span class="_ _3"></span>于<span class="_ _2"> </span><span class="ff2">voronoi<span class="_ _5"> </span></span>图<span class="_ _3"></span>的<span class="_ _3"></span>最<span class="_ _3"></span>小化<span class="_ _3"></span>围<span class="_ _3"></span>捕算<span class="_ _3"></span>法<span class="_ _3"></span>，</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">该算法在多追捕者追捕多个逃逸者的场景中表现出色。<span class="_ _6"></span>本文将此算法应用于电动汽车与主动</div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">配电网的交互中，以实现电力系统的优化管理。</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">三、<span class="ff2">Matlab<span class="_ _2"> </span></span>复现与注释</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">以下是关于该算法在<span class="_ _2"> </span><span class="ff2">Matlab<span class="_ _2"> </span></span>中实现的一段示例代码及其注释：</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">```matlab</div><div class="t m0 x1 h2 y12 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _2"> </span><span class="ff1">初始化设置：追捕者（</span>EVs<span class="ff1">）和逃逸者（例如潜在的负载需求或资源等）的个数定义如下：</span></div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">n_pursuers = 5; <span class="_ _7"> </span>% <span class="_ _8"> </span><span class="ff1">定义追捕者（</span>EVs<span class="ff1">）的数量</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">n_evaders = 3; <span class="_ _9"> </span>% <span class="_ _8"> </span><span class="ff1">定义逃逸者（负载需求）的数量</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _8"> </span><span class="ff1">定义场景中每个元素的初始位置（以坐标表示）</span></div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">initial_positions = [...]; <span class="_ _a"> </span>% <span class="_ _8"> </span><span class="ff1">填入追捕者和逃逸者的初始位置坐标</span></div><div class="t m0 x1 h2 y17 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _8"> </span><span class="ff1">基于<span class="_ _2"> </span></span>Voronoi<span class="_"> </span><span class="ff1">图的围捕算法计算</span></div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _8"> </span><span class="ff1">初始化<span class="_ _2"> </span></span>Voronoi<span class="_"> </span><span class="ff1">图，根据位置和数量进行计算</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">voronoi_map = calculate_voronoi(initial_positions);</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">% <span class="_ _8"> </span><span class="ff1">算法迭代过程开始，此处省略了详细的迭代逻辑和条件判断，仅以伪代码表示：</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">while (not all evaders captured) {</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>% <span class="_ _8"> </span><span class="ff1">根据<span class="_ _2"> </span></span>Voronoi<span class="_"> </span><span class="ff1">图计算每个追捕者的最优移动方向和速度</span></div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>optimal_move = calculate_optimal_move(voronoi_map, n_pursuers, n_evaders);</div><div class="t m0 x1 h2 y1e ff2 fs0 fc0 sc0 ls0 ws0"> </div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>% <span class="_ _8"> </span><span class="ff1">更新追捕者的位置并计算对<span class="_ _2"> </span></span>Voronoi<span class="_"> </span><span class="ff1">图的影响（可能需要额外的坐标转换等）</span></div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>updated_positions = update_positions(pursuers_current_positions, optimal_move);</div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>updated_voronoi_map = update_voronoi(voronoi_map, updated_positions);</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>