System identification of permanent magnet machines and its applications to inter-turn fault detection

Abstract

Permanent magnet machines are of high power density, high efficiency, small weight, and high reliability, and hence have found extensive applications. This paper employs system identification methods for stator winding fault detection and isolation, under noisy measurement data. Algorithms, estimation accuracy, and convergence properties are established. Simulation studies demonstrate the algorithms and their detection capability and reliability. Simulation results are used to illustrate potential usage of the methods.

Modeling and Diagnostic of Permanent Magnet Synchronous Machine under Insulation Failure Condition

“…The fault diagnosis of electrical machines has received an intense amount of research interest during 30 years. Reducing maintenance costs and preventing unscheduled down-times, which result in losses of production and financial incomes and due to their utility in safety-sensitive applications, are the priorities of electrical drives manufacturers and operator [5][6][7]. In fact, correct diagnosis and early detection of incipient faults requires the development of an accurate model for electrical machine, able to simulate electrical faults and the application of an effective diagnosis technique.…”