Search

Permanent magnet synchronous machines work with controllers and controller parameters affect the machine's voltages and currents and, thus, the diagnosis of stator winding fault. One such parameter is the current controller bandwidth that affects the harmonic content in these signals. In the literature, the second harmonics in both $dq$ -axis currents and voltages have been used for fault detection, and the main advantage is that interturn short-circuit (ITSC) winding fault with a few shorted turns, specially one turn, can be detected. One indicator that combines the fault information in voltages and currents is the instantaneous reactive power (IRP). However, it has been shown that IRP does not perform robustly with respect to current controller bandwidth. The main contribution of this article is that it proposes and validates a new mathematical function for ITSCs detection, Rayleigh quotient, which combines the fault information present in voltages and currents to make ITSCs detection robust to current controller bandwidth. The validations from both cosimulation and experiment show that the proposed method is robust to the bandwidth and can detect ITSC faults faster than IRP. [ABSTRACT FROM AUTHOR]

Different faults in permanent magnet synchronous machines (PMSMs) cause various and independent changes to the machine magnetic flux distribution, which affect the machine parameters and performance. These changes are reflected in the machine flux linkages and can be estimated from the machine voltages. In this paper, we continue the work of Haddad et al., where a method was proposed to detect and separate three types of faults in PMSMs: 1) static eccentricity; 2) demagnetization; and 3) turn-to-turn short circuit. The method proposed here is based on using the change in the commanded $d$- and $q$-axis voltages, while the machine is operating at steady state, for fault detection and separation. Simulation tests using finite element analysis (FEA) software (MAGSOFT-Flux2D) were performed under healthy and the tested fault conditions on a three-phase ten-pole fractional-slot concentrated-winding PMSM. An analytical method is proposed to estimate the commanded voltages at any operating condition, and a classification algorithm is implemented for fault detection and classification. Experimental tests were carried out and compared with FEA. The effects of the variation in the operating speed and temperature were simulated and tested to validate the detection method under different operating conditions. [ABSTRACT FROM PUBLISHER]

Open-switch faults in inverter-driven electric drives have recently gained increasing attention in the scientific community. Since these faults may lead to undesired effects, their fast and unambiguous detection is desirable. This paper proposes a detection algorithm for open-switch faults, which is based on the observation of the current control deviation and a subsequent test procedure. This algorithm detects the fault immediately after its occurrence within a few switching periods, even during transient operation. The proposed method is tested via simulations and experiments at different operating points of a surface permanent magnet drive. [ABSTRACT FROM PUBLISHER]

In this paper, the motor current/voltage signature analysis and linear discriminant analysis (LDA) are evaluated with respect to the accuracy to detect the status of permanent magnet synchronous machines (PMSMs) whether it is healthy or faulted, determine the type of that fault, and estimate the severity in the case of static eccentricity or turn-to-turn short-circuit fault. Three types of faults are discussed: static eccentricity, turn-to-turn short circuit, and partial demagnetization fault. Two-dimensional finite element analysis (FEA) is used to model and simulate the machine under healthy and faulted conditions. Fast Fourier transform is applied to the phase voltage or current signals to obtain the frequency spectrum. A combination of the amplitude of the harmonics of the stator voltage or current signals are used as detailed features for the classifier for fault detection. LDA is chosen as a classification method for both detecting the fault and estimating its severity. Two different winding types of PMSMs are tested: a concentrated and a distributed winding machine. To validate the simulation results, experiments at different operational points are carried out and the results are compared with the sFEA. [ABSTRACT FROM PUBLISHER]

Fault diagnosis and its extensions to failure prognosis aim to develop methods that decrease outages and thus increase reliability. Among these methods are redundancies and changes of topology, power levels, and control algorithms. Since diagnosis methods are imperfect, false positives and negatives, as well as delayed fault diagnosis, may occur. Although the diagnosis of a fault can lead to appropriate maintenance, the estimation of the time to failure through prognosis can lead to the timely mitigation of the fault and, in turn, can extend the lifetime and reliability of the drive. In this paper, we present a new approach to the mitigation of permanent-magnet ac motors that uses a failure prognosis method to predict failures and the remaining useful life and uses the output of the prognosis algorithm to decide when to modify the system and mitigate the fault. A methodology to calculate the mean time between failures with and without mitigation is also presented. The proposed approach is illustrated through a simple example of how the prognosis of failure due to a developing intermittent open circuit in one of the phases increases the drive reliability. [ABSTRACT FROM PUBLISHER]

This paper compares the detection methods of static eccentricity in Interior Permanent Magnet Synchronous Machines (IPMSM). Four methods are discussed: The first method uses shift in the voltages in d–q plane to detect fault. The second method uses shift in peak of the incremental inductance curve for fault detection. The third method uses the combined information of harmonics present both in current and voltage to detect the fault. This makes the detection robust with respect to current controller bandwidth. Finally, the fourth method used for detection includes measuring vibrations using accelerometers. It is shown that all four methods detected static eccentricity. These methods are compared on the basis of utility of fault detection under online or offline conditions and under saturated conditions. For all four methods the machine was tested at healthy, 25% and 50% static eccentricity levels. Two-dimensional (2-D) Finite element analysis was used for simulating machine under healthy and faulty cases. The experiments were performed by controlling the machine using Labview Real-time. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous machines designed with single-layer fractional slot concentrated windings are inherently fault tolerant; however, this does not eliminate the occurrence of winding failures. The ability to detect pending failures during operation provides added reliability. In this paper, an online fault detection technique is developed for high resistance inter-turn winding faults. It is shown through analysis, simulation and experiment that variations are present in the DC reference voltages prior to the presence of a short-circuit. [ABSTRACT FROM AUTHOR]