Calculation of eransients in inhomogeneous lines by &amp;#x201C;the method of straight lines&amp;#x201D;

Кабанова, Т. В.; Kuleshova, E O; Isaev, Yu. N.; Шишка, Н. В.; Shishkovskaya, Yulia V.

doi:10.1109/meacs.2014.6986903

Cited by 5 publications

(2 citation statements)

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“…The system of nonlinear differential equations was derived based on Lagrange's equation, Kirchhoff's second law and Newton's second law of motion (4), which was solved numerically and it previously had been reduced to a set of first order differential equations for the accuracy increase of calculation [6]:…”

Section: Experimental Partmentioning

confidence: 99%

Simulation of the electromagnetic drive of the vacuum switch

Васильева

Budko

Lavrinovich

2015

2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

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The paper studies the method of the physical processes research versus presented equivalent circuit of the electromagnetic drive of the vacuum switch. The problem of the difficult configuration system magnetic field calculation with the subsequent calculation of inductance is considered. Discrete values of inductance with spline approximation were transformed to the continuous values. Calculation of inductance was carried out through magnetostatic energy in different time intervals. The decision of the differential equations system of voltage and current balance in the circuit is received. The connection with mechanical processes in system is established. The energy balance was formed. Different types of energy dissipation, energy transfer processes and transformations of energy modes were taken into account. The study was carried out in MathCAD wrapper using the Runge-Kutta method and MATLAB wrapper using the developed algorithm. The test of a model for adequacy was completed by calculation energy balance for system.

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Section: Experimental Partmentioning

confidence: 99%

Simulation of the electromagnetic drive of the vacuum switch

Васильева

Budko

Lavrinovich

2015

2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS)

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“…Today the neural network is widespread. It can be used in control system of electric drives, for the calculation of transient processes in inhomogeneous lines, in direct torque control and others [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Building the Structure and the Neuroemulator Angular Velocity's Learning Algorithm Selection of the Electric Drive of TVR-IM Type

Kozlova

Bolovin

2015

AMM

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Today, one of the most common ways to control smooth starting and stopping of the induction motors are soft-start system. To ensure such control method the use of closed-speed asynchronous electric drive of TVR-IM type is required. Using real speed sensors is undesirable due to a number of inconveniences exploitation of the drive. The use of the observer based on a neural network is more convenient than the use of the real sensors. Its advantages are robustness, high generalizing properties, lack of requirements to the motor parameters, the relative ease of creation. This article presents the research and selection of the best learning algorithm of the neuroemulator angular velocity of the electric drive of TVR-IM type. There were investigated such learning algorithms as gradient descent back propagation, gradient descent with momentum back propagation, algorithm of Levenberg – Marquardt, scaled conjugate gradient back propagation (SCG). The input parameters of the neuroemulator were the pre treatment signals from the real sensors the stator current and the stator voltage and their delay, as well as a feedback signal from the estimated speed with delay. A comparative analysis of learning algorithms was performed on a simulation model of asynchronous electric drive implemented in software MATLAB Simulink, when the electric drive was running in dynamic mode. The simulation results demonstrate that the best method of learning is algorithm of Levenberg – Marquardt.

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