Yazar "Mutluer, Mumtaz" seçeneğine göre listele

Listeleniyor 1 - 4 / 4
  • Küçük Resim Yok
    Öğe
    Application of a hybrid evolutionary technique for efficiency determination of a submersible induction motor
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2011) Mutluer, Mumtaz; Bilgin, Osman
    Electric motors are the largest consumers of electricity in plants and induction motors constitute nearly two-thirds of them. The replacement of in-service induction motors with more efficient ones is a very important strategy for energy savings and consequently nearly 30 different methods have been used to determine the efficiency of induction motors in the last 2 decades. This paper aims to develop the efficiency determination of a 20 Hp submersible induction motor. Due to getting perfect efficiency of the submersible induction motor, a robust hybrid evolutionary optimization technique, which consists of a genetic algorithm and simulated annealing, is used. The obtained results are compared with genetic algorithm and torque gauge result values and dramatically significant results of our proposed algorithm are observed.
  • Küçük Resim Yok
    Öğe
    Application of Finite Element Method to Determine the Performances of the Line Start Permanent Magnet Synchronous Motor
    (ELSEVIER SCIENCE BV, 2015) Kul, Seda; Bilgin, Osman; Mutluer, Mumtaz
    In this paper, low power line start permanent magnet synchronous motor (LSPMSM) is analyzed and simulationed by finite element method using ANSYS RMXPRT and Maxwell 2D/3D modeling software. Parameters of three motor are changed, are modeled than put to the simulation. While the motors are analyzed, motor dimensions aren't changed. After motors are modeled by RMXPRT, the models are transposed to maxwell and observed the magnetic field distribution. The torque and efficiency curve are acquired and compared with each other. Consequently, the results are evaluated in terms of feasibility for the industrial area in terms of efficiency and torque (C) 2015 The Authors. Published by Elsevier Ltd.
  • Küçük Resim Yok
    Öğe
    Comparison of stochastic optimization methods for design optimization of permanent magnet synchronous motor
    (SPRINGER LONDON LTD, 2012) Mutluer, Mumtaz; Bilgin, Osman
    This study presents design optimization of permanent magnet synchronous motor by using different artificial intelligence methods. For this purpose, three stochastic optimization methods-genetic algorithm, simulated annealing, and differential evolution-were used. Motor design parameters and efficiency results obtained by the artificial intelligence methods were compared with each other. The results were later checked by finite element analysis. Consequently, the motor efficiencies obtained from the algorithms have high accuracy. Approaches strategies of the artificial intelligence algorithms are quite sufficient and remarkable for design optimization of permanent magnet synchronous motor. The differential evolution is better and more reliable optimization method nevertheless.
  • Küçük Resim Yok
    Öğe
    An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm
    (TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2016) Mutluer, Mumtaz; Bilgin, Osman
    The artificial bee colony algorithm is one of the latest stochastic methods based on swarm intelligence. The algorithm simulates the foraging behavior of honeybees. The structure of the algorithm is quite simple and its coding is very easy. This paper proposes a design optimization based on geometrical variables to obtain a highly efficient surface mounted permanent magnet synchronous motor with concentrated winding by use of the artificial bee colony algorithm. Input parameters for the algorithm are the geometrical variables of the motor. This approach is more advantageous than finite element analysis requiring a long period of time. Results of the artificial bee colony algorithm are compared with results of a genetic algorithm and checked with a commercial design program. The results emphasize the effectiveness of the algorithm on the design optimization of the permanent magnet synchronous motor.