A Survey on Fault Diagnosis and Fault Tolerant Methodologies for Permanent Magnet Synchronous Machines

Bhuiyan, Erphan A.; Akhand, Md. Maeenul Azad; Das, Sajal K.; Ali, Mohamad Wijayanuddin; Tasneem, Zinat; Islam, Md. Rashidul; Saha, Dip Kumar; Badal, Faisal R.; Ahamed, Md. Hafiz; Moyeen, Sumaya I.

doi:10.1007/s11633-020-1250-3

Cited by 45 publications

(25 citation statements)

References 134 publications

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“…Similarly, in [22] only selected FDD methods are analyzed for electrical faults occurring in PMSM drive such as open-or short-circuit faults of power devices, windings and inverter legs, unstable voltages and sensor faults. However, the references concerning the FDD methods for electrical, magnetic and mechanical faults of PMSM are only listed, not all are evaluated.…”

Section: B Contribution Of This Papermentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSM) are currently very topical due to the increasing use of these drives in safety-critical devices. Every year, more and more articles on this subject are published. Therefore, the aim of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods using the analysis of control structure signals for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of PMSM drives failures. Based on the presented literature analyses, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

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Section: B Contribution Of This Papermentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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“…Electronics 2022, 11, x FOR PEER REVIEW 2 of 24 "sensorless" or "encoderless" control of the PMSM drive where the rotor position and speed are obtained without using encoders or resolvers [10][11][12][13][14][15]. Sensorless control of five-phase motors has been researched in the last couple of years.…”

Section: Sensored Speedmentioning

confidence: 99%

“…Unfortunately, using these sensors will reduce the reliability of the whole drive system as these sensors interfere with the noise. Therefore, many researchers have been directed to the "sensorless" or "encoderless" control of the PMSM drive where the rotor position and speed are obtained without using encoders or resolvers [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control of Seven-Phase PMSM Drives Using NSV-SVPWM with Minimum Current Distortion

Saleh

Sumner

2022

Electronics

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This paper introduces three different algorithms to track the saliency in the seven-phase PMSM to obtain sensorless speed control. These algorithms are based on measuring the amount of change of the stator currents when different active vectors are implemented using the Near Six Vectors-Space Vector Pulse Width Modulation (NSV-SVPWM). Then, a comparison between these algorithms in terms of the Total Harmonics Distortion (THD) associated with implementing each algorithm is presented. The increase in the current distortion in these algorithms is related to the fact that narrow active vectors in these algorithms should be extended to tmin (8us) for proper measurement in the current dynamics due to the oscillation in the currents. However, since the time duration of these active vectors in NSV-SVPWM will be 2.52 times longer than others, it is found that using these long active vectors will result in minimum current distortion. Simulation results are provided to investigate the effect of using the dynamic response of different active vectors on the THD of the current. Moreover, the results demonstrate the effectiveness of the proposed control technique to maintain the performance of the seven-phase PMSM motor drive post the failure in the speed sensor.

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“…Faults found in the power distribution system are the defect in the system which distracted the current from the intended path. ese faults create an abnormal situation that reduces the insulation strength of the conductors which prompts excessive damage to the distribution system [33]. As a microgrid is the integration of numerous DGs, the same types of faults arise which reduce the efficiency of the system.…”

Section: Faults Encounteredmentioning

confidence: 99%

A Deep Learning through DBN Enabled Transmission Line Fault Transient Classification Framework for Multimachine Microgrid Systems

Bhuiyan

Akhand

Fahim

et al. 2022

International Transactions on Electrical Energy Systems

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The reliable operation of power systems becomes a formidable job these days due to the high amount of complexities in the expanded power system networks. The power system networks often comprise microgrids that encounter over 80% of faults due to their exposure to unpredictable weather conditions, which reduce the insulation strength of the conductors which damaged the distribution system. Therefore, detection, classification, and location of such faults in the distributing systems are a must to ensure the flawless operation of the power systems. Machine learning methodologies are getting more attention to detect these types of faults due to their capability to handle complex fault information. Nevertheless, the obstacles are even now on the board as the traditional machine learning techniques rely on oversimplified frameworks that are incapable of analyzing a wide range of latent and explicit parameters and are also time-consuming. In this work, a unique defect diagnosis technique based on a multiblock deep belief network (DBN) and the fundamental discrete wavelet transform (DWT) is proposed, allowing the architecture to identify the deterministic reconstructing throughout its inputs. This method enables a strong multilevel generative network to utilize fault-related properties, decodes high variability functionalities, and requires minimal previous knowledge. The suggested approach is validated using a wide set of input data at various sampling frequencies. To evaluate the efficiency of DBN, a benchmark methodology based on the International Electrotechnical Commission (IEC) standard was used. White Gaussian Noise (WGN) was also implemented to test the envisioned network’s resilience. The findings show that the approach is capable of executing exact diagnosis procedures.

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A Survey on Fault Diagnosis and Fault Tolerant Methodologies for Permanent Magnet Synchronous Machines

Cited by 45 publications

References 134 publications

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Sensorless Control of Seven-Phase PMSM Drives Using NSV-SVPWM with Minimum Current Distortion

A Deep Learning through DBN Enabled Transmission Line Fault Transient Classification Framework for Multimachine Microgrid Systems

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