A Simple Torque Estimator for In-Service Efficiency Determination of Induction Motors

Stopa, Marcelo Martins; Saldanha, Marcos A.; Luiz, Alex-Sander Amável; Baccarini, Lane Maria Rabelo; Lacerda, George A. M.

doi:10.1109/tia.2018.2835411

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…In conclusion, several approaches suggest low-error load torque estimation, particularly when operating close to or above 30% of the nominal load. However, according to the authors of [ 2 ], estimating torque accurately at low loads, especially below 30% of the TIM’s nominal load, remains a challenge. This study delves into the model’s training and generalization capabilities specifically at low load levels, evaluating torque estimation below 30% of the TIM’s nominal load, thus examining estimations below 12 Nm.…”

Section: Discussionmentioning

confidence: 99%

“…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 ]. In addition, as cited by [ 3 , 4 ], knowing the torque value helps professionals and engineers better understand potential faults or malfunctions in the machine [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…During the past decades, many researchers have dedicated themselves to investigating new techniques and approaches for estimating load torque in induction motors [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Dias,

Fontenele

2024

Sensors

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“…If it is possible to change the voltage under the rated frequency f 1N, a no-load test to measure mechanical loss can be performed. If it is difficult to do this, such as in a general-purpose inverter, mechanical loss is assumed to be 1.2% of the rated output PN (12)- (13) . Tmec is determined by dividing the mechanical loss by the mechanical rated synchronous angular frequency.…”

Section: Pre-determination Methodsmentioning

confidence: 99%

Torque Estimation of Variable-Speed Induction Motors Without Torque and Rotational Speed Meters

Yamamoto,

Hirahara

2024

IEEJ Journal IA

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This paper presents a torque estimation method for variable speed induction motors for factory tests that does not use a torque or rotational speed meter. A new algorithm, which takes into account the torque reduction component due to stray load loss that varies with the main flux level and rotational speed, is proposed to accurately estimate the torque, which is based on the cross product of the stator flux linkage and current vectors. Pre-determining two parameters, the parallel equivalent iron loss resistance and percentage stray load loss, is the key to accurate torque estimation using this algorithm. The authors also show a method to identify the two parameters only from a rated voltage no-load test and nameplate values. This allows determining the percentage stray load loss for individual test machines without performing actual load testing with torque and rotational speed measurements. The proposed method is carried out on V/f-controlled test machines fed by three types of voltage waveforms: sine, square, and pulse width modulation drives. The performance of the proposed torque estimation is verified by comparing the estimated and measured torques.

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“…There are several methods for torque and efficiency estimation in literature, but as cited by [5,6], the air-gap torque (AGT) is one of the most successful technique [7], although this method requires at least four measurements, two voltages and two currents, which are not always available, and the nameplate information. In addition, as described by [5], some kind of compensation for offset integration error is required in AGT method, which is sometimes cumbersome.…”

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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A Simple Torque Estimator for In-Service Efficiency Determination of Induction Motors

Cited by 16 publications

References 12 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

Torque Estimation of Variable-Speed Induction Motors Without Torque and Rotational Speed Meters

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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