Title: Efficiency determination of induction motor and its sensitivity analysis towards parameter variation

Authors: S. Anil Chandrakanth; Thanga Raj Chelliah; S.P. Srivastava

Addresses: Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Haridwar District – 247667, Uttarakhand, India ' Water Resource Development and Management Department, Indian Institute of Technology Roorkee, Roorkee, Haridwar District – 247667, Uttarakhand, India ' Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Haridwar District – 247667, Uttarakhand, India

Abstract: The exact knowledge of some of the induction motor parameters is very important to implement efficient control schemes and to determine the efficiency. These parameters can be obtained by no-load test that is not easily possible for the motors working in process industries where continuous operation is required. Here, particle swarm optimisation is used for in situ efficiency determination of induction motor (5 hp) without performing no-load test. The part load efficiency and power factor can be improved through loss model controller where the motor excitation is adjusted in accordance with load and speed. Induction motor parameters vary with temperature. So, parameters obtained by conducting no load and blocked rotor test may vary with loading of induction motor. LMC is sensitive to parameter variation and its performance is affected when parameters change. An attempt is made to gain a deeper physical insight into the induction motor operation through sensitivity analysis of its equivalent circuit parameters. This study reveals the effect each of the circuit parameters namely Rs, Rr, Ls, Lr and Lm has on torque, speed, flux and DC-link power, respectively.

Keywords: induction motors; particle swarm optimisation; PSO; efficiency determination; efficiency standards; loss model control; parameter variation; part load efficiency; power factor; torque; speed; flux; DC-link power.

DOI: 10.1504/IJAISC.2014.062826

International Journal of Artificial Intelligence and Soft Computing, 2014 Vol.4 No.2/3, pp.144 - 163

Accepted: 01 Dec 2013
Published online: 19 Jun 2014 *

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