The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis.
Interturn short circuits are among the most critical faults in permanent magnet synchronous motor drives, as they combine localized heating in the shorted stator phase with electrical asymmetry that distorts the current feedback used for torque-producing control. This article proposes a control-based mitigation method enabling the post-fault operation of standard three-phase motor drives without additional dedicated hardware. Using the diagnostic features inferred from standard control-loop signals, the method augments the field-oriented control structure with two mechanisms: resistive-loss-limited current-reference generation and reconstruction of the torque-producing current components in the feedback path. The reference generator is derived from a discrete-time post-fault model and minimizes resistive losses, whereas the feedback reconstruction provides fault-free torque-producing current components as controlled variables of the current loop. The experimental validation has demonstrated reductions of up to 23-36% in the fault-induced increase in the input power and 18-27% in the local segment-loss increase, while confirming real-time adaptation to progressive fault aggravation emulated by stepped changes in the short circuit resistance.
L. Zezula, M. Kozovský, Ludek Buchta et al.· arXiv.org· 0 citations
Fault detection is one of the most common studies on wind turbines. In this case, the doubly fed induction generator (DFIG) is specifically analyzed. The fault cases analyzed are: inter-turn short circuit and open circuit, in addition to normal operating conditions. The K-means algorithm was used for analysis and classification. The data set is obtained from multiple simulations in MATLAB/Simulink, in which the stator resistance (Rs) and stator inductance (Ls) were varied. From these simulations, the current and voltage signals are processed using tools such as the Park transform, stator current imbalance, and harmonic analysis to obtain relevant characteristics for the classification of each case. It is known that Rs is a determining parameter in fault detection: in a short circuit, Rs tends to fall below its nominal value due to the appearance of a low-impedance path, while in an open circuit, Rs tends to rise above the nominal value due to the interruption of the conductor. The results obtained indicate that the K-means algorithm, together with the proposed methodology, are efficient in classifying the different stator states. This suggests that the proposed solution, based on the results, could be effective and economical for wind turbine monitoring.
Anthony Molina, Andres Romero, G. Suvire· Simposio Internacional sobre...· 0 citations
applications, brushless DC (BLDC) motor drives are becoming more and more significant because of their dependability, efficiency, and flexibility with regard to renewable energy systems (RESs). In this investigation, a novel fault detection technique for three-phase inverter (3PhI) switch resistance fault of brushless DC motor drive with permanent magnet (PMBLDC) is presented. Diagnostics of BLDC motor inverter switch resistance failure (BISRF) is based on the Fast Fourier Transform (FFT) analysis of the stator current of the BLDC motor supplied by the 3PhI. To detect the BISRF, the impact of BISRF on the fundamental, DC, and harmonic distortion, and also on subharmonics has been investigated over a range of fault levels. Depending on the best fit parameter, the BISRF has been identified. A BISRF detection algorithm also has been presented.
Results showed that the DSP-based online condition monitoring system was more accurate and better at detecting faults than earlier methods, making it a good fit for usage in industrial applications.
Mohan P. Thakre, Badal Kumar, Supriya Nilesh Thakur et al.· Bulletin of Electrical Engin...· 0 citations
Induction machine fault diagnosis using current spectral analysis is a well-established diagnostic technique based on the identification of the characteristic harmonic components generated in the machine current by each type of fault. However, one of the main problems with the application of this technique to the diagnosis of rotor asymmetry faults in induction machines is that the fault components have much lower amplitudes than the fundamental component and can be very close to it, making their detection difficult, especially in transient regimes. To improve the detection of fault harmonics, this work proposes a new diagnostic current signal, the backward-rotating transient current signal, which is generated in the time domain using the Hilbert transform of the stator currents and is free of the strong influence of the fundamental component. The key novelty of this proposal is the combination of the analytical current signals and the symmetrical components method, which produces a purely backward-rotating transient current signal that cannot be obtained using the raw phase current signals. This proposal is presented theoretically and validated in transient regime using a commercial induction motor with rotor asymmetries.
J. Martínez-Román, R. Puche-Panadero, Carla Terron-Santiago et al.· IEEE Transactions on Instrum...· 0 citations
The outcomes prove that the integrated technical framework (time-domain features + FR + SVM) provides zero false alarms and a balanced diagnostic system that combines computational speed with high precision.
H. Zaimen, T. Thelaidjia, Makhlouf Chouki et al.· International Journal of Ele...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
