基于粒子群算法优化的永磁同步电机多参数精准辨识系统,基于粒子群算法的永磁同步电机多参数辨识PSO PMSG1仿真程序参考文献《改进粒子群算法的永磁同步电机多参数辨识》,采用粒子群算法与sim
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基于粒子群算法优化的永磁同步电机多参数精准辨识系统,基于粒子群算法的永磁同步电机多参数辨识PSO PMSG[1]仿真程序参考文献《改进粒子群算法的永磁同步电机多参数辨识》,采用粒子群算法与simulink模型结合的方式,对永磁同步电机进行多参数辨识。程序以定子绕组电阻、d轴电感、q轴电感和永磁体磁链四个参数作为输入参数,以定子dq轴电压作为输出,通过辨识模型电压与测量电压的偏差作为目标函数,从而实现参数的精准辨识。[2]适应度函数以辨识模型与实际测量值之间的误差平方和最小为目标,适应度函数值越小,其辨识模型电压与测量电压越接近,待辨识参数和实际值也越接近。[3]算法流程主要是通过粒子群算法调用simulink仿真模型,通过输入计算输出值和适应度值,通过循环优化出最佳参数。先运行.m 文件后运行仿真,不然会报错包含参考文献,默认 2018 版本。谢谢理解,核心关键词:基于粒子群算法的永磁同步电机多参数辨识; PSO; PMSG; 辨识模型; 适应度函数; 循环优化; 最佳参数; 仿真程序; 电压偏差; 参数辨识精度。,基于PSO算法的PMSG多参数辨识仿真程序 <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/90340300/2/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/90340300/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">基于粒子群算法的永磁同步电机多参数辨识</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">一<span class="ff2">、</span>引言</div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">随着永磁同步电机<span class="ff3">(<span class="ff4">PMSG</span>)</span>在工业<span class="ff2">、</span>交通<span class="ff2">、</span>航空等领域的广泛应用<span class="ff3">,</span>其性能优化和参数辨识显得尤为</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">重要<span class="ff2">。</span>参数辨识的准确性直接影响到电机的控制精度和运行效率<span class="ff2">。</span>本文将介绍一种基于粒子群算法<span class="ff3">(</span></div><div class="t m0 x1 h2 y5 ff4 fs0 fc0 sc0 ls0 ws0">PSO<span class="ff3">)<span class="ff1">的永磁同步电机多参数辨识方法<span class="ff2">。</span></span></span></div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">二<span class="ff2">、</span>粒子群算法<span class="ff3">(<span class="ff4">PSO</span>)</span>简介</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">粒子群算法是一种全局搜索优化算法<span class="ff3">,</span>通过模拟鸟群<span class="ff2">、</span>鱼群等生物群体的行为规律<span class="ff3">,</span>实现全局寻优<span class="ff2">。</span></div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">该算法具有简单易实现<span class="ff2">、</span>收敛速度快等优点<span class="ff3">,</span>在许多领域得到了广泛应用<span class="ff2">。</span></div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">三<span class="ff2">、</span>基于<span class="_ _0"> </span><span class="ff4">PSO<span class="_ _1"> </span></span>的永磁同步电机多参数辨识模型</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">本文所提出的辨识模型<span class="ff3">,</span>采用<span class="_ _0"> </span><span class="ff4">PSO<span class="_ _1"> </span></span>与<span class="_ _0"> </span><span class="ff4">Simulink<span class="_ _1"> </span></span>模型相结合的方式<span class="ff3">,</span>对永磁同步电机进行多参数辨</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">识<span class="ff2">。</span>模型以定子绕组电阻<span class="ff2">、<span class="ff4">d<span class="_ _1"> </span></span></span>轴电感<span class="ff2">、<span class="ff4">q<span class="_ _1"> </span></span></span>轴电感和永磁体磁链四个参数作为输入参数<span class="ff3">,</span>以定子<span class="_ _0"> </span><span class="ff4">dq<span class="_ _1"> </span></span>轴电</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">压作为输出<span class="ff2">。</span>通过辨识模型电压与测量电压的偏差作为目标函数<span class="ff3">,</span>实现参数的精准辨识<span class="ff2">。</span></div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">四<span class="ff2">、</span>仿真程序实现</div><div class="t m0 x1 h2 ye ff4 fs0 fc0 sc0 ls0 ws0">1.<span class="_ _2"> </span><span class="ff1">适应度函数设计</span></div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">适应度函数以辨识模型与实际测量值之间的误差平方和最小为目标<span class="ff2">。</span>适应度函数值越小<span class="ff3">,</span>其辨识模型</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">电压与测量电压越接近<span class="ff3">,</span>待辨识参数和实际值也越接近<span class="ff2">。</span>这为<span class="_ _0"> </span><span class="ff4">PSO<span class="_ _1"> </span></span>算法提供了优化的目标<span class="ff2">。</span></div><div class="t m0 x1 h2 y11 ff4 fs0 fc0 sc0 ls0 ws0">2.<span class="_ _2"> </span><span class="ff1">算法流程</span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">算法流程主要是通过<span class="_ _0"> </span><span class="ff4">PSO<span class="_ _1"> </span></span>调用<span class="_ _0"> </span><span class="ff4">Simulink<span class="_ _1"> </span></span>仿真模型<span class="ff3">,</span>输入计算输出值和适应度值<span class="ff3">,</span>通过循环优化出</div><div class="t m0 x1 h2 y13 ff1 fs0 fc0 sc0 ls0 ws0">最佳参数<span class="ff2">。</span>具体步骤如下<span class="ff3">:</span></div><div class="t m0 x1 h2 y14 ff3 fs0 fc0 sc0 ls0 ws0">(<span class="ff4">1</span>)<span class="ff1">初始化粒子群</span>,<span class="ff1">包括粒子的位置<span class="ff2">、</span>速度和权重等</span>;</div><div class="t m0 x1 h2 y15 ff3 fs0 fc0 sc0 ls0 ws0">(<span class="ff4">2</span>)<span class="ff1">将粒子群输入到<span class="_ _0"> </span><span class="ff4">Simulink<span class="_ _1"> </span></span>仿真模型中</span>,<span class="ff1">计算输出值和适应度值</span>;</div><div class="t m0 x1 h2 y16 ff3 fs0 fc0 sc0 ls0 ws0">(<span class="ff4">3</span>)<span class="ff1">根据适应度值对粒子进行评估</span>,<span class="ff1">更新粒子的速度和位置</span>;</div><div class="t m0 x1 h2 y17 ff3 fs0 fc0 sc0 ls0 ws0">(<span class="ff4">4</span>)<span class="ff1">循环执行步骤</span>(<span class="ff4">2</span>)<span class="ff1">和</span>(<span class="ff4">3</span>),<span class="ff1">直到达到预设的迭代次数或满足终止条件</span>;</div><div class="t m0 x1 h2 y18 ff3 fs0 fc0 sc0 ls0 ws0">(<span class="ff4">5</span>)<span class="ff1">输出最优解</span>,<span class="ff1">即最佳参数值<span class="ff2">。</span></span></div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">五<span class="ff2">、</span>结论</div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div>