Classification and comparison of rotor temperature estimation methods of squirrel cage induction motors

Nikbakhsh, Amir; Izadfar, Hamid Reza; Jazaeri, Mostafa

doi:10.1016/j.measurement.2019.03.072

Cited by 32 publications

(10 citation statements)

References 95 publications

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“…Based on Fig 8 , the errors are lies in the range of [–4,4] with a nearly identical cycle. This is might be related to the uncertainty issue of the induction motor parameter [ 30 ]. The uncertainty error is regularly distributed, which indicates that the range of its contributions is the same.…”

Section: Resultsmentioning

confidence: 99%

Modeling of high voltage induction motor cooling system using linear regression mathematical models

et al. 2022

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Achieving reliable power efficiency from a high voltage induction motor (HVIM) is a great challenge, as the rigorous control strategy is susceptible to unexpected failure. External cooling is commonly used in an HVIM cooling system, and it is a vital part of the motor that is responsible for keeping the motor at the proper operating temperature. A malfunctioning cooling system component can cause motor overheating, which can destroy the motor and cause the entire plant to shut down. As a result, creating a dynamic model of the motor cooling system for quality performance, failure diagnosis, and prediction is critical. However, the external motor cooling system design in HVIM is limited and separately done in the past. With this issue in mind, this paper proposes a combined modeling approach to the HVIM cooling system which consists of integrating the electrical, thermal, and cooler model using the mathematical model for thermal performance improvement. Firstly, the development of an electrical model using an established mathematical model. Subsequently, the development of a thermal model using combined mathematical and linear regression models to produce motor temperature. Then, a modified cooler model is developed to provide cold air temperature for cooling monitoring. All validated models are integrated into a single model called the HVIM cooling system as the actual setup of the HVIM. Ultimately, the core of this modeling approach is integrating all models to accurately represent the actual signals of the motor cooler temperature. Then, the actual signals are used to validate the whole structure of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) analysis. The results demonstrate the high accuracy of the HVIM cooling system representation with less than 1% error tolerance based on the industrial plant experts. Thus, it will be helpful for future utilization in quality maintenance, fault identification and prediction study.

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Section: Resultsmentioning

confidence: 99%

Modeling of high voltage induction motor cooling system using linear regression mathematical models

et al. 2022

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“…(a) Schematic of an electric motor emphasising points of interest for grease lubrication. Adapted with permission from Reference 34 (Elsevier). (b) Schematic illustration of a high‐speed transmission system representing lubrication injection points.…”

Section: Application Of Greases In Ev Industry and Current Challengesmentioning

confidence: 99%

Latest developments in designing advanced lubricants and greases for electric vehicles—An overview

Shah¹,

Gashi²,

Rosenkranz

2022

Lubrication Science

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According to the Environmental Protection Agency, transportation is the largest contributor of greenhouse gas emissions (28% of total emissions). Electric vehicles have remarkably grown in popularity and represent a greener future for the automotive industry. This growth has prompted lubricant and grease technology to adapt to an entirely new environment, which is exposed to new factors including external electric currents/fields and extreme temperatures and pressures originating from electric motors and power electronics. Consequently, novel lubricants and greases need to be developed and explored to ultimately improve fuel efficiency and performance. Nano-additives have offered exceptional opportunities to enhance electrical, thermal and tribological properties of the lubricants and greases used. It is thus vital to fully explore and understand the effects of nanoparticles' addition to lubricants and greases, as well as the mechanisms by which improvements are obtained. This perspective summarises the recent trends of developing lubricants and greases for electric vehicles.

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“…As a result, 19 thermal system parameters need to be identified as specified in Table 2: Table 2. Thermal system parameters (Param) to be identified, relating to the values of the lumped parameters (Values) in (2).…”

Section: Inverse Thermal Identification Of the Induction Machinementioning

confidence: 99%

“…Thermal prediction of electric motors is becoming increasingly important in the drive for higher power density, energy efficiency and cost reduction [1]. Accurate temperature estimation of motor hotspots is key for condition monitoring and fault detection [2], as well as motor control [3,4]. As the stator windings are surrounded by thermally vulnerable insulation material, real-time prediction of the temperature of the windings allows enhancement of the motor end-of-life duration.…”

Section: Introductionmentioning

confidence: 99%

Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model

et al. 2019

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Accurate temperature estimation inside an electrical motor is key for condition monitoring, fault detection, and enhanced end-of-life duration. Additionally, thermal information can benefit motor control to improve operational performance. Lumped-parameter thermal networks (LPTNs) for electrical machines are both flexible and cost-effective in computation time, which makes them attractive for use in real-time condition monitoring and integration in motor control. However, the accuracy of these thermal networks heavily depends on the accuracy of its system parameters, some of which are difficult to calculate analytically or even empirically and need to be determined experimentally. In this paper, a methodology for the thermal condition monitoring of long-duration transient and steady-state temperatures in an induction motor is presented. To achieve this goal, a computationally efficient second-order LPTN for a 5.5 kW squirrel-cage induction motor is proposed to apprehend the dominant heat paths. A fully thermally instrumented induction motor has been prepared to collect spatial and temporal temperature information. Using the experimental stator and rotor temperature data collected at different motor operating speeds and torques, the key thermal parameter values in the LPTN are identified by means of an inverse methodology that aligns the simulated temperatures of the stator windings and rotor with the corresponding measured temperatures. Validation results show that the absolute average thermal modelling error does not exceed 1.45 • C with maximum absolute error of 2.10 • C when the motor operates at fixed speed and torque. During intermittent motor-loading operation, a mean (maximum) stator temperature error of 0.38 • C (0.92 • C) was achieved and mean (maximum) rotor errors of 2.11 • C (3.40 • C). These results show the validity of the proposed thermal model but also its ability to predict in real time the temperature variations in stator and rotor for condition monitoring and motor control.

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Classification and comparison of rotor temperature estimation methods of squirrel cage induction motors

Cited by 32 publications

References 95 publications

Modeling of high voltage induction motor cooling system using linear regression mathematical models

Modeling of high voltage induction motor cooling system using linear regression mathematical models

Latest developments in designing advanced lubricants and greases for electric vehicles—An overview

Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model

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