Dynamic Computer Model of Traction Asynchronous Motor

Omelchenko, Evgeniy; Khramshin, Timur R.; Tanich, Vasily; Kozhevnikov, Igor

doi:10.1109/peami.2019.8915408

Cited by 9 publications

(3 citation statements)

References 1 publication

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“…Typically, sensitivity analysis serves as an introduction to optimization studies. Its results are used to justify the choice of decision variables (Omelchenko et al, 2019) and to evaluate the quantitative and qualitative influence of parameters on the intended functions that determine dynamic parameters of the systems. The easiest way to find the intended function derivatives is to use the finite difference method (German-Galkin and Kozak, 2018).…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of a mechatronic drive system with an induction motor

Cao¹,

Liu²

2023

Journal of Theoretical and Applied Mechanics

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The paper presents research findings in the modelling and optimization of dynamic parameters of mechatronic systems with an induction motor. A mathematical model was developed to analyze currents in dynamic states of squirrel-cage rotors in the case of a line-to-line fault. The findings were verified experimentally using calculations for a 1.5 kW three-phase induction motor. The equations for a stationary 0x, 0y coordinate system relating to the stator were derived. The set of design variables selected in the optimization process contained parameters describing design features of the gear shafts and control units settings.

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Section: Introductionmentioning

confidence: 99%

Dynamic analysis of a mechatronic drive system with an induction motor

Cao¹,

Liu²

2023

Journal of Theoretical and Applied Mechanics

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“…Electrical machines (EM) are built from a stator, which is the static part of the EM consisting of phase-shifted coiled poles, and a rotor, which is the rotary part constructed depending on the tasks for which the machine is intended. AMs are most often used as motor-driven machines [3][4][5] in the automotive industry [6,7], construction [8], elevators [9], etc., while SMs are most often used to produce electricity in fossil fuel power plants [10][11][12] or in renewable energy plants, such as hydroelectric power [13,14] and wind power plants [15,16]. For energy to be transmitted from the producer to the final consumer, it must be of an alternating current (AC) [17].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

et al. 2022

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A synchronous machine is an electro-mechanical converter consisting of a stator and a rotor. The stator is the stationary part of a synchronous machine that is made of phase-shifted armature windings in which voltage is generated and the rotor is the rotating part made using permanent magnets or electromagnets. The excitation current is a significant parameter of the synchronous machine, and it is of immense importance to continuously monitor possible value changes to ensure the smooth and high-quality operation of the synchronous machine itself. The purpose of this paper is to estimate the excitation current on a publicly available dataset, using the following input parameters: Iy: load current; PF: power factor; e: power factor error; and df: changing of excitation current of synchronous machine, using artificial intelligence algorithms. The algorithms used in this research were: k-nearest neighbors, linear, random forest, ridge, stochastic gradient descent, support vector regressor, multi-layer perceptron, and extreme gradient boost regressor, where the worst result was elasticnet, with R2 = −0.0001, MSE = 0.0297, and MAPE = 0.1442; the best results were provided by extreme boosting regressor, with R2¯ = 0.9963, MSE¯ = 0.0001, and MAPE¯ = 0.0057, respectively.

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“…For affine nonlinear systems, such as motors [35][36], the nonlinear systems can be linearized using the method of state variable feedback, which is a nonlinear feedback linearization method based on the theory of differential geometry [37]. Unlike local linear approximations using Taylor series expansions, nonlinear feedback linearization does not ignore any higher-order nonlinear terms in the linearization process, so the linearization is accurate and global.…”

Section: Introductionmentioning

confidence: 99%

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

Zhong

Ma²,

Hao

et al. 2021

IEEE Access

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To allow the hoisting motor drive system of a crane to track a load torque quickly, a linearization method was used to transform a motor nominal dynamics model into two decoupled linear rotor speed and flux linkage subsystems. The method based on the theory of differential geometry was a precise feedback method. Two active disturbance rejection controllers (ADRCs) with identical structures were designed for the rotor speed and flux linkage subsystems. The extended state observer of the ADRC could estimate the unmodeled dynamics of the motor, the variation of motor parameters due to heating, and the unknown disturbances of the motor system to determine the total disturbances of the system. A closed-loop system with ADRC and an open-loop system were compared. The motor's full-load starting time was reduced by about 50%. When the motor operated smoothly at different load rates and the rated load was suddenly applied, the electromagnetic torque fluctuation range did not exceed 20 N• m. The rotor flux was always stable at the reference value. The motor speed decreased, but the amount of decrease did not exceed 7 rad/s. The closed-loop system had a significant energy-saving effect during the motor's starting process. The power saving rate was about 55%-59% if the motor started with a light load. The power saving rate could reach 71% if the motor started with a heavy load. The ADRC system could accurately estimate the unknown model of the rotor speed and flux linkage subsystems, and adapt to parameter variations of the motor stator and rotor resistance in the range of ±10%. Motor: Te, TL Electromagnetic and load torques. TN Nominal torque. ET Electromagnetic torque tracking error. np Motor's pole logarithm. Je Rotational inertia of the rotor. n Rotor speed. ω Electric angular velocity. ωr Mechanical angular velocity. α, β Static two-phase coordinate axis.

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Dynamic Computer Model of Traction Asynchronous Motor

Cited by 9 publications

References 1 publication

Dynamic analysis of a mechatronic drive system with an induction motor

Dynamic analysis of a mechatronic drive system with an induction motor

Estimation of Excitation Current of a Synchronous Machine Using Machine Learning Methods

Research on the Hoisting Motor Drive System’s Active Disturbance Rejection Control and Energy Consumption for a Crane

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