• Title of article

    Estimation of induction motor parameters using COA algorithm

  • Author/Authors

    Joodaki, Abbas Department of Electrical Engineering - Neyshabur Branch - Islamic Azad University, Neyshabur, Iran , Shojaei, Ali Asghar Department of Electrical Engineering - Neyshabur Branch - Islamic Azad University, Neyshabur, Iran , Lotfi, Hossein Department of Electrical Engineering - Neyshabur Branch - Islamic Azad University, Neyshabur, Iran

  • Pages
    14
  • From page
    117
  • To page
    130
  • Abstract
    Induction motors are one of the most widely used machines that are used in industrial motion control and home systems. On the other hand, the optimal determination parameters have a direct and significant effect on their efficiency, longevity, and performance. Determining the initial parameters of these machines by the classic method is time-effective and costly. Therefore in recent years, the use of computer processing such as simulation, heuristical algorithm, ANN, etc. has become common as an alternative method in this field. Cuckoo Optimization Algorithm (COA) is a relatively new algorithm and it is less used to solve this nonlinear problem. In this article, the parameter of the induction machine will be estimated using COA. COA. In the following, the results obtained from the algorithm will be compared with the results obtained from several examples of conventional algorithms. Objective functions are defined as minimizing the true values of the relative error between the measured and estimated torques of the machine in different slips. Objective functions have been used to optimize the parameters of the induction motor with an approximate equivalent circuit and the induction motor with the exact circuit. The final results show the accuracy of operation and very good convergence speed of the algorithm in solving the problem.
  • Farsi abstract
    فاقد چكيده فارسي
  • Keywords
    COA , Parameter estimation , Induction motor
  • Journal title
    Journal of Advances in Computer Research
  • Serial Year
    2020
  • Record number

    2702356