2022
DOI: 10.37391/ijeer.100240
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Performance Analysis of Fault Tolerant Operation of PMSM using Direct Torque Control and Fuzzy Logic Control

Abstract: Electromagnets have traditionally been used in all drives. Because they take up space, the size of the machine grows in tandem with increased torque and it’s rating thereby lowering its energy efficiency. If the rotor winding is replaced with permanent magnets, the motor will reverse. The recent improvement of magnetic materials resulted in a reduction in motor size and more effective use of redial space. Permanent Magnet Synchronous Motors (PMSM) have a high-power factor, are extremely durable, and require al… Show more

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Cited by 6 publications
(4 citation statements)
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“…L. Jenila et al [40] proposed a scheduling algorithm for wireless multimedia sensor. S. Kulkarni et al [41] have done the performance analysis for fault tolerant operation of PMSM to increase system reliability. N. Thapliyal et al [42] developed a load balance min-min scheduling algorithm for load balancing in cloud computing inspired by the foraging activity of honey bees.…”
Section: ░ 2 Literature Reviewmentioning
confidence: 99%
“…L. Jenila et al [40] proposed a scheduling algorithm for wireless multimedia sensor. S. Kulkarni et al [41] have done the performance analysis for fault tolerant operation of PMSM to increase system reliability. N. Thapliyal et al [42] developed a load balance min-min scheduling algorithm for load balancing in cloud computing inspired by the foraging activity of honey bees.…”
Section: ░ 2 Literature Reviewmentioning
confidence: 99%
“…Permanent Magnet Synchronous Motor (PMSM) is becoming popular in Electric Vehicle (EV) domain because of its merits such as good dynamic performance [1], high efficiency [2], high power density [3] and almost no maintenance [4][5][6][7]. Under running condition different effects such as temperature [8], saturation effects [9,10] and Voltage Source Inverter (VSI) non-linearities [11] affect the performance of the EV system.…”
mentioning
confidence: 99%
“…The input parameters of the CNN for the proposed method are shown in The confusion matrix for the output of CNN is shown in table 2 and figure 8. One of the main metrics utilized in CNN designs for the classification phase is the confusion matrix[32,33].…”
mentioning
confidence: 99%