Interturn Fault Detection in IPMSMs: Two Adaptive Observer-based Solutions

TL;DR

This paper introduces two adaptive observer-based methods for online detection of inter-turn short-circuit faults in IPMSMs, providing explicit fault current estimates and demonstrating effectiveness through simulations.

Contribution

It presents two novel observer solutions for fault detection in IPMSMs, with one offering fast exponential detection and the other finite-time convergence, both with online parameter estimation.

Findings

Both observers detect faults effectively in simulations.

The second observer ensures finite convergence time.

Explicit fault current estimation improves detection accuracy.

Abstract

In this paper we address the problem of online detection of inter-turn short-circuit faults (ITSCFs) that occur in permanent magnet synchronous motors (PMSMs). We propose two solutions to this problem: (i) a very simple linear observer and (ii) a generalized parameter estimation based observer, that incorporates a high performance estimator -- with both observers detecting the short-circuit current and the fault intensity. Although the first solution guarantees the detection of the fault exponentially fast, the rate of convergence is fully determined by the motor parameters that, in some cases, may be too slow. The second observer, on the other hand, ensures finite convergence time under the weakest assumption of interval excitation. To make the observers adaptive, we develop a parameter estimator that, in the case of isotropic PMSMs, estimates on-line (exponentially fast) the resistance and inductance of the motor. It should be underscored that, in contrast with existing observers (including the widely popular Kalman filter) that provide indirect information of the fault current, our observers provide explicit one -- namely the amplitude of the fault current. The performance of both observers, in their linear and generalized parameter estimation-based versions, is illustrated with realistic simulation studies.