Mathematical Modeling, Simulation and Experimental Testing of Interior-Mount LSPMSM Under Stator Inter-Turn Fault

Maraaba, Luqman S.; Al-Hamouz, Z.; Abido, M. A.

doi:10.1109/tec.2018.2886137

Cited by 29 publications

(20 citation statements)

References 28 publications

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“…The experimental investigation was carried out on a 1.0-hp, 60-Hz, 344 turns per phase, and 4 poles interior mount LSPMSM. The simulation study was carried out using the validated mathematical model in [1]. Equations (6)(7)(8)(9)(10)(11)(12)(13)(14), as shown at the bottom of the next page, represent the final mathematical model in qd0 frame.…”

Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

“…The use of line start permanent magnet synchronous motors (LSPMSMs) in industry such as in pumps, fans, compressors, and other constant speed applications is in its early stages [1]. LSPMSMs are considered among the most promising types of motors, due to their high efficiency, high operational power factor, self-starting, high power density, high operational torque, and low operational temperature [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…Since the number of LSPMSMs used in the industry is increasing, there is subsequent need for maintenance programs. As such, it is crucial to develop a diagnostic tool that predicts the faults in their early stages [1]. It is worth mentioning that the focus of the reported research on faults diagnosis of LSPMSM was mostly on rotor faults and demagnetization [7]- [12].…”

Section: Introductionmentioning

confidence: 99%

“…It is worth mentioning that the focus of the reported research on faults diagnosis of LSPMSM was mostly on rotor faults and demagnetization [7]- [12]. Recently, a few research works concentrated on stator winding faults [1], [13]. This research work is a serious attempt to close this gap and investigate the stator inter-turn fault in the interior-mount LSPMSMs.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

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Section: Experimental Setup and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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“…The healthy model of LSPMSM in the qd0 reference frame is a well-known model [25][26][27][28][29][30][31][32]. Equations (1)- (10) represent the transient model for the LSPMSM.…”

Section: Mathematical Model Of Lspmsmmentioning

confidence: 99%

Comprehensive Parameters Identification and Dynamic Model Validation of Interior-Mount Line-Start Permanent Magnet Synchronous Motors

et al. 2019

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The application of line-start permanent magnet synchronous motors (LSPMSMs) is rapidly spreading due to their advantages of high efficiency, high operational power factor, being self-starting, rendering them as highly needed in many applications in recent years. Although there have been standard methods for the identification of parameters of synchronous and induction machines, most of them do not apply to LSPMSMs. This paper presents a study and analysis of different parameter identification methods for interior mount LSPMSM. Experimental tests have been performed in the laboratory on a 1-hp interior mount LSPMSM. The measurements have been validated by investigating the performance of the machine under different operating conditions using a developed qd0 mathematical model and an experimental setup. The dynamic and steady-state performance analyses have been performed using the determined parameters. It is found that the experimental results are close to the mathematical model results, confirming the accuracy of the studied test methods. Therefore, the output of this study will help in selecting the proper test method for LSPMSM.

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