Modeling faulted switched reluctance motors using evolutionary neural networks

Belfore, Lee A.; Arkadan, A.A.

doi:10.1109/iecon.1994.397972

Cited by 6 publications

(6 citation statements)

References 17 publications

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“…The ANN algorithm is employed in [23] and [24] for prediction of motor performance under faulty conditions, which is relatively difficult and complex, and the variations of the control parameters are not considered in this ANN-based model. An offline table of the current states under different faults is presented in [25] by detecting the upper freewheeling bus current and excitation bus current, and the converter circuit is modified for the current sensors installation.…”

Section: Comparison Of the Proposed Fault Diagnosis Scheme With Exmentioning

confidence: 99%

“…Compared to the existing schemes in [23]- [27], and [29], the proposed fault diagnosis scheme uses only a dc-link current sensor without any additional diagnosis devices and much change to the circuitry, which will considerably reduce the volume and complexity of the motor drive. The proposed scheme is found to be more accurate and easier to implement for fault diagnosis purposes by extracting the fault coefficient from the detected current through WPD algorithm.…”

Section: Comparison Of the Proposed Fault Diagnosis Scheme With Exmentioning

confidence: 99%

“…The artificial neural networks (ANNs) and evolutionary algorithms-based model [23], and the ANNs and genetic algorithms-based model [24] are employed to predict the performance of SRM drives under normal and faulty operating conditions. In [25], two schemes by monitoring the chopped bus current and by detecting the upper freewheeling bus current and excitation bus current are proposed and compared in detail for power switch faults.…”

mentioning

confidence: 99%

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Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Gan

Yang

et al. 2016

IEEE Trans. Energy Convers.

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Abstract-Power converters are a key, but vulnerable component in switched reluctance motor (SRM) drives. In this paper, a new fault diagnosis scheme for SRM converters is proposed based on the wavelet packet decomposition (WPD) with a dc-link current sensor. Open-and short-circuit faults of the power switches in an asymmetrical half-bridge converter are analyzed in details. In order to obtain the fault signature from the phase currents, two pulse-width modulation signals with phase shift are injected into the lower-switches of the converter to extract the excitation current, and the WPD algorithm is then applied to the detected currents for fault diagnosis. Moreover, a discrete degree of the wavelet packet node energy is chosen as the fault coefficient. The converter faults can be diagnosed and located directly by determining the changes in the discrete degree from the detected currents. The proposed scheme requires only one current sensor in the dc link, while conventional methods need one sensor for each phase or additional detection circuits. The experimental results on a 750-W three-phase SRM are presented to confirm the effectiveness of the proposed fault diagnosis scheme.Index Terms-Current sensor, discrete degree, fault diagnosis, node energy, power converter, pulse-width modulation (PWM), switched reluctance motor (SRM), wavelet packet decomposition (WPD).

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Section: Comparison Of the Proposed Fault Diagnosis Scheme With Exmentioning

confidence: 99%

Section: Comparison Of the Proposed Fault Diagnosis Scheme With Exmentioning

confidence: 99%

mentioning

confidence: 99%

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Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Gan

Yang

et al. 2016

IEEE Trans. Energy Convers.

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“…Some of them, as [4][5][6][7] discussed analytical models based on motor geometry data, while others were dedicated to the SRM models which employ magnetic equivalent circuits [8,9]. Other models are based on finite element analysis (FEA) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…This method is simple but is not very accurate. Recently, magnetic characteristics calculation based on fuzzy logic or artificial neural network (ANN) techniques [11][12][13][14][15], have been reported. The accuracy of these models strongly depends on the amount of given flux linkage-current-position data and on the fuzzy rules or the existing knowledge.…”

Section: Introductionmentioning

confidence: 99%

Two Simple Analytical Models, Direct and Inverse, for Switched Reluctance Motors

Someşan

Padurariu²,

Viorel³

2013

PIER M

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Abstract-This paper presents two simple analytical models of the switched reluctance motor. The first model is constructed on two flux linkage-current characteristics: the aligned position one, calculated via finite element analysis (FEA), and the unaligned position characteristic calculated by using motor geometry data. The second model is based on three flux linkage-current characteristics: the aligned, unaligned and average one, obtained by employing the FEA. In both cases the direct and inverse models are defined. The models consider the core nonlinearity and the influence of the rotor position on the motor behavior. The estimated magnetizing and torque characteristics are compared with that calculated via two dimensions FEA for a switched reluctance motor (SRM) sample and with the test bench obtained ones. The merits and the drawbacks of the models are evinced.

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Development of a simple nonlinear switched reluctance motor model using measured flux linkage data and curve fit

Chan¹,

Weldon²

IAS '97. Conference Record of the 1997 IEEE Industry Applications Conference Thirty-Second IAS Annual Meeting

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Modeling faulted switched reluctance motors using evolutionary neural networks

Cited by 6 publications

References 17 publications

Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Wavelet Packet Decomposition-Based Fault Diagnosis Scheme for SRM Drives With a Single Current Sensor

Two Simple Analytical Models, Direct and Inverse, for Switched Reluctance Motors

Development of a simple nonlinear switched reluctance motor model using measured flux linkage data and curve fit

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