Microprocessor based protection of induction motors using thermal, mechanical, and skin effect electrical models to predict motor temperature rise

This study investigates induction motor performance during a variety of system and load conditions. A computer simulation is used to characterize the motor behavior during normal operation, lockedrotor case, high inertia loading, and overloading conditions. Motor temperature at critical points is estimated. A microprocessor protection scheme based on motor temperature is proposed. Previous studies assume a linear relationship between motor impedance and frequency when calculating motor temperature. In this study an electrical model, based on Maxwell's equations, is used in conjunction with a mechanical model, to represent the motor during dynamic state conditions. The model accounts for the rotor bar skin effect as the motor speed changes. Motor losses, computed using the electrical model, are fed to a thermal model. Stator, rotor, and core temperatures are calculated. Thermal limit curves are presented. Protection strategies are investigated. A microprocessor based scheme is recommended. The scheme responds to motor temperature and trips the motor only at critical conditions. The scheme is optimal, simple, and easy to implement. The diagnostic capability of this work is also valuable. For example, the motor acceleration time for a specified load can be calculated by this computer simulation. Thus, if the acceleration time changes it shows that there is a problem in a motor bearing or in some other mechanical part.

Grantor dc:publisher

University of Tennessee at Chattanooga

Author dc:creator

• Moharari, Nader S.

Contributors dc:contributor

• Eltom, Ahmed H.
• Carney, Terrence M.; Parten, Clifford R.; Smullen, Stephanie A.
• College of Engineering and Computer Science

• Electric motors, Induction

Licence

• https://rightsstatements.org/page/InC/1.0/?language=en

Language dc:language

English, eng

Identifier

LB2369.2 .M61 1989

OAI identifier oai:identifier

oai:scholar.utc.edu:theses-1589