Abstract
This paper describes three different ways of transformer modeling for inrush current simulations. The developed transformer models are not dependent on an integration step, thus they can be incorporated in a state-space form of stiff differential equation systems. The eigenvalue propagations during simulation time cause very stiff equation systems. The state-space equation systems are solved by usingA- andL-stable numerical differentiation formulas (NDF2) method. This method suppresses spurious numerical oscillations in the transient simulations. The comparisons between measured and simulated inrush and steady-state transformer currents are done for all three of the proposed models. The realized nonlinear inductor, nonlinear resistor, and hysteresis model can be incorporated in the EMTP-type programs by using a combination of existing trapezoidal and proposed NDF2 methods.
Highlights
The transformer represents one of the essential elements in power systems
This paper describes three different ways of transformer modeling for inrush current simulations
Proper modeling of the transformer is very important in different transient and steady-state power systems simulations
Summary
The transformer represents one of the essential elements in power systems. Proper modeling of the transformer is very important in different transient and steady-state power systems simulations. The nonlinearity of the transformer iron core is the most important parameter in simulations of lowfrequency transients, such as transformer inrush current, ferroresonance, temporary overvoltages during transformer energizations, load rejections, and harmonic analysis. All these transients belong to a frequency range of up to 1 kHz [1]. The winding parameters are linear, whilst the iron core parameters describe nonlinear phenomena during the analysis of low-frequency transformer transients. The modeling of nonlinear or hysteresis inductor and nonlinear core loss resistor can be done by using the curve fitting procedure for the approximation of nonlinearity or by using piecewise linear representation of the nonlinear curve. Piecewise linearization has disadvantages related to overshooting effects [27]
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