Double Three-phase Induction Machine Modeling for Internal Faults Simulation

Abstract

Multi-phase induction motors present excellent characteristics for faulty tolerant operation. Among them, six-phase induction motors, which are among the most used, exhibit two types of configuration: the double three-phase and the six-phase with single neutral. The double three-phase presents the advantage of reducing harmonics with its symmetric winding. This article presents a new modeling of the double three-phase induction machine for internal faults simulation. The developed model is composed by two sets of three-phase stator windings forming two stars. The model considers an arbitrary displacement (α) between stator stars, allowing the simulation of this type induction machines with different configurations, with 60° displacement used herein. The simulations of internal faults, such as stator windings or rotor faults, are both considered in the proposed model, allowing the machine study under abnormal conditions. The double three-phase induction machine model was fully implemented in real coordinates, making it possible to simulate stator and rotor faults without being necessary to change system equations coordinate. Several examples allow verifying the characteristics of the proposed model and its application for internal fault analysis. Experimental results are also presented to validate the obtained simulation results.

Keywords:

• induction motors
• modeling
• fault diagnosis
• fault tolerance
• stator winding fault
• rotor fault
• six-phase induction machine
• double three-phase induction machine

Additional information

Notes on contributors

Daniel Foito

Daniel Foito received his Dipl. Ing. and M.S. in electrical engineering from the Instituto Superior Técnico (IST), Technical University of Lisbon, Lisbon, Portugal, in 1993 and 2002, respectively. Since 1997, he has been a member of the teaching staff at the Electrical Engineering Department of Superior Technical School of Setúbal–Polytechnic Institute of Setúbal. Presently he is an adjoint professor, teaching power electronics and electric drives. His research interests include renewable energy generation, electrical machines, electric drives, electric vehicle, fault diagnosis, and fault-tolerant operation.

José Maia

José Maia graduated in 1986 in electrical engineering from the IST, Technical University of Lisbon, Lisbon, Portugal, where he also received his M.Sc. and Ph.D. in electrical engineering in 1990 and 1996, respectively. Currently, he is a coordinator professor in the Electrical Engineering Department of Superior Technical School of Setúbal–Polytechnic Institute of Setúbal, Portugal. His research areas are electrical drives, power electronics, converter control, renewable energy systems, and electrical vehicles.

V. Fernão Pires

V. Fernão Pires received his B.S. in electrical engineering from the Institute Superior of Engineering of Lisbon, Portugal, in 1988 and his M.S. and Ph.D. in electrical and computer engineering from the Technical University of Lisbon, Portugal, in 1995 and 2000, respectively. Since 1991, he has been a member of the teaching staff in the Electrical Engineering Department of Superior Technical School of Setúbal–Polytechnic Institute of Setúbal. Presently he is a professor, teaching power electronics and control of power converters. He is also a researcher at INESC-ID. His research interests include electrical drives, power electronics, converter control and renewable energy systems.

João F. Martins

João F. Martins graduated in electrical engineering from the IST, Technical University of Lisbon, in 1990. He received his M.Sc. and Ph.D. in electrical engineering at the same institute, respectively, in 1996 and 2003. Currently, he is with the Department of Electrical Engineering, Faculty of Sciences and Technology, Universidade Nova de Lisboa, Portugal. He has published more than 30 scientific articles in refereed journals and books and more than 70 articles in refereed conference proceedings. His research areas include control of electrical drives, advanced learning control techniques for electromechanical systems, grammatical inference learning algorithms, fault diagnosis and fault-tolerant operation, teaching, alternative energies, and intelligent buildings.