Stator Current Spectral Content of Inverter-Fed Cage Rotor Induction Motor

Joksimović, Gojko; Zajec, Peter; Tessarolo, Alberto; Ambrozic, V.; Rihar, Andraž

doi:10.1109/access.2022.3154481

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…Rotating electrical machines are the main industrial assets responsible for energy conversion in productive segments, in a variety of applications. In this sense, three-phase squirrel cage induction motors (TIMs) have been the most employed machines in the last decades in the industrial environment, being responsible for operating in different types of drives [ 1 ]. Despite the widespread use of TIMs, their measured or estimated torque and efficiency are important information for energy diagnosis purposes and to match the motor load [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Dias,

Fontenele

2024

Sensors

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This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine.

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Section: Introductionmentioning

confidence: 99%

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Dias,

Fontenele

2024

Sensors

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“…The power and torque versus speed characteristics of traction motors play a critical role in influencing the operating performance of EVs [5]. The inclusion of integrated pulse-width modulation (PWM) inverters within the EV drive system introduces additional time harmonics, which when combined with space harmonics, negatively affect the electromagnetic performance [6], [7]. Therefore, it becomes necessary to consider the effects of time and space harmonics during the induction machine design stage to improve electromagnetic torque performance across the entire speed range.…”

Section: Introductionmentioning

confidence: 99%

“…These models use instantaneous rotor-position-dependent inductances and are dependent on the airgap flux density and its space harmonics. However, assumptions of infinite magnetic permeabilities of the magnetic cores and idealized winding distributions led traditional WF models to overestimate the airgap flux densities and inductances, thereby negatively impacting the electromagnetic torque calculation accuracy [6], [18].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Field-Based Induction Machine Modeling Incorporating Space and Time Harmonic Effects

Guruwatta Vidanalage,

Lombardi,

Tjong

et al. 2024

IEEE Access

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Integration of the impacts of time and space harmonics simultaneously during the design stages of inverter-fed induction machines (IMs) is crucial for the accurate calculation of electromagnetic torque (EM) and ripples. Traditional magnetic field-based models offer an analytical approach for determining the EM torque in IMs by calculating inductances. However, by assuming infinite core permeabilities, these models neglect the impact of the core magnetomotive force (MMF) drops due to the difficulties in accurately calculating these drops and the impracticality of isolating the contribution from each phase, which is essential for inductances calculations. These factors contribute to deficiencies in this modeling approach, which become more noticeable when the combined impacts of time and space harmonics on MMF drop calculations are also disregarded. Therefore, this paper introduces a novel magnetic-field-based model to predict the torque and torque ripples of inverter-fed induction motors by addressing the above limitations. This involves modifying the turns and winding function, and calculating the core MMF drops based on the timely variation of non-sinusoidal core flux densities, considering their major and minor flux-density loop effects. Consequently, the associated energy is used to calculate the net available energy, thereby enhancing torque calculations. Compared to the experimental results obtained from an 11kW prototyped induction motor, the proposed model exhibits notable enhancements, achieving average accuracies of 96.4% for average torque and 94.51% for torque ripples, in contrast to the respective traditional model accuracies, 81.1% and 45.1%.

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“…Three-phase induction motors (TIM) still play an important role in several applications around the world, even after decades of intense use and technical improvements [1]. In recent years, the field of rotating electric machines condition monitoring is drawing the interest of researchers and many estimating torque would be useful especially for medium and large capacity motors.…”

Section: Introductionmentioning

confidence: 99%

Torque estimation in three-phase induction motors under broken bars and voltage variations using airgap flux measurement and a machine learning model

Dias,

Custódio da Costa,

Fontenele

2023

Meas. Sci. Technol.

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This paper proposes a new approach for load torque estimation in squirrel cage induction motors using airgap flux measurement, by means of a Hall effect sensor installed between two stator slots of the machine. The rotor speed was estimated from the Hall sensor signal using a previous method designed by the authors, and the root mean square and mean value were also computed from the Hall signal to serve as inputs to a multi-layer perceptron model.The positioning of the Hall sensor inside the machine was also investigated during different experiments for torque prediction and considered as an additional input for the regression task. The present methodology does not require motor parameters for torque prediction. This research has been validated using some simulated and experimental results for different motor operational conditions. The performance metrics disclosed a good torque prediction for the machine fed not only by a rated voltage, but also by overvoltage/undervoltage power supply and with broken rotor bars, even for low loads. Both experimental and simulation results showed a mean absolute percentage error lower than 4% for a 7.5 kW induction motor.

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Stator Current Spectral Content of Inverter-Fed Cage Rotor Induction Motor

Cited by 12 publications

References 19 publications

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors

Magnetic Field-Based Induction Machine Modeling Incorporating Space and Time Harmonic Effects

Torque estimation in three-phase induction motors under broken bars and voltage variations using airgap flux measurement and a machine learning model

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