...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Industrial Electronics >Efficiency Estimation of the Induction Machine by Particle Swarm Optimization Using Rapid Test Data With Range Constraints
【24h】

Efficiency Estimation of the Induction Machine by Particle Swarm Optimization Using Rapid Test Data With Range Constraints

机译：利用范围约束的快速测试数据粒子群优化效率估计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Temperature rise of an induction machine increases losses and decrease in the efficiency. The temperature rise depends on the machine design. It takes several hours after starting a machine to reach to thermal stability. In most of the in situ efficiency estimation methods, it is required to obtain the operating data of the machine at a thermally stable condition, which needs a long running time of the machine. In this paper, a method based on a particle swarm optimization (PSO) algorithm is proposed, which can estimate the machine efficiency at different loads with thermal stability. The machine operation data at the first 30 min after the start rather than data at a thermal stability condition are used in the method. The proposed algorithm utilizes two approaches to predict a full-load temperature at a thermally stable condition. The first approach is based on the insulation class of the machine and uses the equivalent circuit. The second approach is based on the trend of the temperature rise in the first 30 min of running the machine after the start. Furthermore, a method is proposed to narrow the parameters range, which helps the PSO to converge to the right answer. All results are validated by the experimental results.
机译：感应机的温度升高会增加损耗并降低效率。温度升高取决于机器设计。开始机器达到热稳定性后需要几个小时。在大多数原位效率估计方法中，需要在热稳定状态下获得机器的操作数据，这需要机器的长时间运行时间。本文提出了一种基于粒子群优化（PSO）算法的方法，可以估计具有热稳定性的不同负载的机器效率。在该方法中使用在开始而不是热稳定性条件下的开始而不是数据之后的前30分钟的机器操作数据。所提出的算法利用两种方法以在热稳定的状态下预测全负荷温度。第一种方法基于机器的绝缘类并使用等效电路。第二种方法是基于在开始后运行机器的前30分钟的温度升高的趋势。此外，提出了一种方法来缩小参数范围，这有助于PSO会聚到正确的答案。所有结果都是通过实验结果验证的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号