A stator turn fault in a safety-critical drive application must be detected at its initial stage and imperatively requires an evasive action to prevent a serious accident caused by an abrupt interruption in the drive s operation. However, this is much challenging for the case of an interior permanent magnet synchronous motor (IPMSM) drives because of the presence of the permanent magnets that cannot be turned off at will. This work tackles the problem of increase the stator turn fault tolerance of IPMSM drives in safety-critical applications. This objective is achieved by an on-line turn fault detection method and a simple turn fault-tolerant operating strategy.
In this work, it is shown that a stator turn fault in a current-controlled voltage source inverter-driven machine leads to a reduced fundamental positive sequence component of the voltage references as compared to the machine without a turn fault for a given torque reference and rotating speed. Based on this finding, a voltage reference-based turn fault detection method is proposed.
In addition, it is also revealed that an adjustment to the level of the rotating magnetic flux in an appropriate manner can yield a significant reduction in the propagation speed of the fault and possibly prevention of the fault from spreading to the entire winding. This would be accomplished without any hardware modification. Based on this principle, a stator turn fault-tolerant operating strategy for IPMSM drives maintaining drive s availability is proposed.
To evaluate these turn fault detection method and fault-tolerant operating strategy, an electrical model and a thermal model of an IPMSM with stator turn faults are derived.
All the proposed models and methods are validated through simulations and experiments on a 10kW IPMSM drive.
Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/16156 |
Date | 02 July 2007 |
Creators | Lee, Youngkook |
Publisher | Georgia Institute of Technology |
Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
Detected Language | English |
Type | Dissertation |
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