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

机译：基于范围约束快速测试数据的粒子群算法在感应电机效率评估中的应用

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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：感应电机的温度升高会增加损耗并降低效率。温升取决于机器设计。启动机器后，需要几个小时才能达到热稳定性。在大多数现场效率估计方法中，需要在热稳定条件下获得机器的运行数据，这需要机器的长时间运行。提出了一种基于粒子群算法的粒子群优化算法，该算法可以估计具有热稳定性的不同负荷下的机械效率。该方法使用启动后前30分钟的机器运行数据，而不是热稳定性条件下的数据。所提出的算法利用两种方法来预测热稳定条件下的满载温度。第一种方法基于机器的绝缘等级，并使用等效电路。第二种方法基于启动后运行机器的前30分钟内温度升高的趋势。此外，提出了一种缩小参数范围的方法，这有助于PSO收敛到正确的答案。所有结果均通过实验结果验证。

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