Inter-Turn Fault Detection in PM Synchronous Machines by Physics-Based Back Electromotive Force Estimation

Sarikhani, Ali; Mohammed, Osama A.

doi:10.1109/tie.2012.2222857

Cited by 152 publications

(50 citation statements)

References 38 publications

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“…The drives' dynamic behavior is simulated using a per-phase equivalent circuit model with the winding inductances and resistances analytical calculated based on the machine geometry and fault location. A simplified thermal model is also grafted into the system model to effectively simulate the dynamic behavior of the machine during healthy, inter-turn SC [1][2][3][4] are increasingly being used in safety critical applications such as in aircraft and other transportation systems. In such applications the necessary system reliability level can be reached by designing for fault tolerance and hence avoiding replicating the complete drive system [5].…”

mentioning

confidence: 99%

Analysis of Vertical Strip Wound Fault-Tolerant Permanent Magnet Synchronous Machines

Arumugam

Hamiti

Brunson

et al. 2014

IEEE Trans. Ind. Electron.

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mentioning

confidence: 99%

Analysis of Vertical Strip Wound Fault-Tolerant Permanent Magnet Synchronous Machines

Arumugam

Hamiti

Brunson

et al. 2014

IEEE Trans. Ind. Electron.

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“…Further derivation of a PN model is very tedious, and compromises accuracy, especially for complex rotor geometries. In [10] and [27] an inductance based model was proposed for inter-turn fault detection in PM synchronous machines. However, IPM machines with buried magnets exhibits high level of magnetic saturation and cross-saturation effects and therefore separation of armature reaction flux linkage from the total flux linkage will incur large error and hence compromises model accuracy [28]- [33].…”

Section: Nomenclaturementioning

confidence: 99%

“…However, IPM machines with buried magnets exhibits high level of magnetic saturation and cross-saturation effects and therefore separation of armature reaction flux linkage from the total flux linkage will incur large error and hence compromises model accuracy [28]- [33]. Moreover the method of extraction of inductances reported in [27] and [34] by energy-perturbation is computationally more demanding requiring twice as many FE computations [32]. A hybrid model for wound-rotor synchronous generator reported in [35] assumes that the machine operates in linear region under healthy condition.…”

Section: Nomenclaturementioning

confidence: 99%

A High-Fidelity Computationally Efficient Transient Model of Interior Permanent-Magnet Machine With Stator Turn Fault

Sen

Wang

Lazari

2016

IEEE Trans. Ind. Electron.

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General nonlinear mapping function between fluxlinkage/ torque to stator currents. I. INTRODUCTION NTERIOR permanent magnet (IPM) machines are increasing being favored as the machine of choice for electric vehicle application due to their high power density, robustness, large constant power speed range and overall high efficiency [1]- [6]. However, due to presence of magnets in the rotor, electrical faults must be quickly detected and mitigating controls initiated to prevent catastrophic failure of the machine. Such a functionality commonly known as "limphome" mode [7] is essential for providing high degree of availability, and reliability demanded in safety critical application such as electric vehicles. In order to develop sensitive fault detection algorithms and fault tolerant control strategies, an accurate transient model of the machine under fault condition is indispensable [8]-[10] at development stage in order to save time and resources spent on experimental testing. This is because many faults such as inter-turn shortcircuit may cause benign changes in terminal voltages and currents. Consequently, it is difficult to detect them in an electrically noisy environment. Inaccurate representation of fault behaviors may lead to a detection algorithm working well in simulations, but not effective in real testing.Several surveys on reliability of industrial motors conducted by Electric Power Research Institute EPRI [11] and IEEE [12]- [15] concluded that stator winding failures accounts for about 21-37% of faults in electrical machines. One of the leading causes of winding failure are inter-turn short-circuit failures which are especially critical, since it leads to a large circulating current in the faulted turns [16]. This gives rise a local hot spot which can cause further insulation failures and ultimately leading to a complete failure of the winding as a phase-ground or phase-to-phase fault [17]. The large circulating current in the faulted turns can also produce irreversible demagnetization of the magnets [18].The modeling of inter-turn short-circuit fault in IPM was treated in [19] [35] assumes that the machine operates in linear region under healthy condition. However, this assumption is not applicable to IPM machines with high level of magnetic saturation [32]. The aim of this paper is to establish an accurate and computationally efficient model of IPM machines under stator turn fault. This is achieved by extracting flux linkage map of the machine under turn fault conditions using offline static FE analysis and combining it with voltage equations of the machine. The method is not limited to IPM machines and the same technique can be used for modeling stator turn faults in passive rotor systems including surface PM machines, switched reluctance machines, switched flux machines and, separately excited machines, such as wound field synchronous machines. This approach enables the full representation of spatial harmonics and magnetic saturation under inter-turn fault and all load conditions and therefore i...

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“…Another example of a diagnostic method related to inter-turn short-circuit is a method that is similar to the MCSA method which involves analysis of back-electromotive force (emf) in the frequency domain. This method is more fully described in [7]. The above mentioned methods, apart from numerous advantages, also have disadvantages, frequency analysis can be used only for stationary signals.…”

Section: Introductionmentioning

confidence: 99%

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

Pietrowski

Górny

2017

Open Physics

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Abstract:Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

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Inter-Turn Fault Detection in PM Synchronous Machines by Physics-Based Back Electromotive Force Estimation

Cited by 152 publications

References 38 publications

Analysis of Vertical Strip Wound Fault-Tolerant Permanent Magnet Synchronous Machines

Analysis of Vertical Strip Wound Fault-Tolerant Permanent Magnet Synchronous Machines

A High-Fidelity Computationally Efficient Transient Model of Interior Permanent-Magnet Machine With Stator Turn Fault

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

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