基于人工蜂群算法的感应电机参数准确估计新方法
New Method for Accurate Parameter Estimation of Induction Motors Based on Artificial Bee Colony Algorithm
AI总结：
本文提出利用人工蜂群算法估计双笼感应电机参数的方法，通过最小化模型与制造商提供的扭矩、电流等参数差异平方和，验证了该方法在效率和精度上的优势。
摘要：
本文提出一种有效的双笼感应电机参数估计方法，采用人工蜂群算法（ABC）计算异步电机电气模型中的未知参数，使模型所得扭矩、电流等参数与制造商提供的数据的平方差之和最小化。为验证所提方法的有效性，结果还与GA、PSO和PAMP的对比结果进行了比较。仿真显示，在所考虑的问题中，ABC算法收敛速度明显快于其他算法，且结果与PAMP相当准确。
英文摘要：
This paper proposes an effective method for estimating the parameters of double-cage induction motors by using Artificial Bee Colony (ABC) algorithm. For this purpose the unknown parameters in the electrical model of asynchronous machine are calculated such that the sum of the square of differences between full load torques, starting torques, maximum torques, starting currents, full load currents, and nominal power factors obtained from model and provided by manufacturer is minimized. In order to confirm the efficiency of the proposed method the results are also compared with those achieved by using GA, PSO, and PAMP. The simulations show that in the problem under consideration ABC converges considerably faster than other algorithms and the results are as accurate as PAMP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
