Klemen Dezelak

Prevention of Iron Core Saturation in Multi-Winding Transformers for DC-DC Converters

This paper deals with prevention of saturation in the iron core of a multi-winding transformer. It is a substantial part of dc-dc converters used in resistance spot welding systems. The discussed resistance spot welding system consists of a semiconductor input converter, a single-phase welding transformer with one primary coil and two secondary coils, and a full-wave output rectifier connected to the transformers secondary coils. The paper shows that the interaction among magnetically nonlinear behavior of the iron core combined with unbalanced parameters of the circuits with the two transformers secondary coils can cause iron core saturation even when elements connected to coils are passive elements. The first part of the paper focuses on analysis of saturation phenomena in multi-winding transformers. It is performed on specially designed laboratory transformer composed of the iron core in the form of two C-shaped segments and modular coils used to form the single-coil primary winding and two-coil secondary winding. Knowledge acquired on the laboratory transformer is applied to develop two different solutions for active prevention of the iron core saturation in multi-winding welding transformers. Both solutions are presented in the second part of the paper.

Artificial Neural Network Applied for Detection of Magnetization Level in the Magnetic Core of a Welding Transformer

This paper deals with the detection of saturation in the magnetic core of a welding transformer which is a part of a middle-frequency direct current (MFDC) resistance spot welding system (RSWS). It consists of an input rectifier, which produces dc bus voltage, an inverter, a welding transformer, and a full-wave rectifier that is mounted on the output of a transformer. During normal RSWS operation welding transformers magnetic core can become saturated due to the unbalanced resistances of both transformer secondary windings and different characteristics of output rectifier diodes, which causes current spikes and over-current protection switch-off of the entire system. In order to prevent saturation of the transformer magnetic core, the RSWS control must detect that the magnetic core is approaching the saturated region. The aim of this paper is to present a reliable method for detection of magnetic core saturation that does not require an additional sensor. It is based on the artificial neural network (ANN). Its input is the measured primary current of the welding transformer. The applied ANN is trained to recognize the waveform of the current spikes in the primary current caused by the magnetic core saturation, which is used for magnetization level detection.

Usage of a Simplified and Jiles–Atherton Model When Accounting for the Hysteresis Losses Within a Welding Transformer

The proposed paper deals with a nonlinear dynamic model of a welding transformer, where the effects of hysteresis losses are accounted for by the simplified method and by the inverse form of the Jiles-Atherton hysteresis model. Both models have been modified in such a way that they can be used when the magnetically nonlinear characteristic of the entire device is determined experimentally. The aforementioned characteristics are given in the form of approximation polynomials. For defining magnetically nonlinear characteristic and both the Jiles-Atherton hysteresis model and the simplified model, an optimization procedure based on the differential evolution is applied for obtaining the parameters of applied approximation polynomials. The main goal of this paper is to evaluate both discussed models regarding their suitability to be included into dynamic model of a welding transformer used in the optimization of the entire resistance spot welding system. The evaluation focuses on achieved accuracy and computational effort required to implement the discussed models.

The Impact of the Voltage Generation Method on Acoustic Noise Emissions Caused by a Welding Transformer

This paper deals with the acoustic noise emissions caused by a welding transformer (WT) operating as part of a middle-frequency direct current resistance spot welding system (RSWS). The WT consists of an iron core, one primary winding, and two secondary windings. The primary winding is supplied by the voltage from the input converter while the full-wave diode output rectifier is connected to the two secondary windings in order to generate a direct welding current. In the case study, the alternating current primary voltage is generated in two different ways, by applying a pulse width modulation and two hysteresis controllers. The aim of this paper is to analyze how the voltage generation method influences the acoustic noise emissions caused by the WT. The analysis is based on the values of the supply current, the welding current, and the iron core flux density measured on a 160 kW industrial WT operating as a part of laboratory RSWS where the supply voltage is generated in two different ways. The results presented in the paper show that proper voltage generation method can substantially reduce the acoustic noise emissions caused by a WT.

GPU-Based Online Optimization of Low Voltage Distribution Network Operation

This paper proposes a parallelized online optimization of low voltage distribution network (LVDN) operation. It is performed on a graphics processing unit (GPU) by combining the optimization procedure with the load flow method. In the case study, performed for the test LVDN with distributed generators (DGs) and controllable loads, differential evolution optimization based on a backward–forward sweep load flow method was parallelized on GPU. The goal of online optimization is to keep the LVDN voltage profile within the prescribed limits, to minimize LVDN losses, and to enable demand response functionality. This is achieved by the optimization determined reference values for the controllable load’s operation, and the reactive power generation, and active power curtailment of DGs. The results show that the parallelized GPU implemented optimization can be significantly faster than similar implementation on a central processing unit, and is, therefore, suitable for the online optimization of the presented LVDN.

Model of photovoltaic power plant with constant resistive load

This paper deals with a mathematical model of a photovoltaic panel, which directly supplies a constant resistive load without inverter cooperation. Later on, this basic theory is used to develop a model of photovoltaic plant working with a constant resistive load. The models correctness is evaluated through the comparison of simulated values and real measured data.

The Impact of Voltage Generation on Harmonic Spectra of Current and Flux Density in the Welding Transformer for a Middle Frequency Resistance Spot Welding System

This paper deals with a middle frequency direct current (MFDC) resistance spot welding system (RSWS). It consists of a semiconductor input converter, a single-phase welding transformer with one primary coil and two secondary coils, and a full-wave output rectifier connected to the transformers secondary coils. The unwanted current spikes in the input converter, caused by interaction among the asymmetrical design of the transformer, its magnetically nonlinear behavior, and unequal characteristics of the diodes in the output rectifier, can cause over-current protection switch-off of the discussed system. These current spikes can be effectively avoided by the closed- loop control of the load current and flux density in the welding transformers iron core. The welding transformer supply voltage can be generated either by the pulse width modulation or by the hysteresis controllers. This paper analyzes the impact of voltage generation method applied in the MFDC RSWS on harmonic spectra of current and iron core flux density responsible for acoustic noise.

The Impact of Iron Core Model on Dynamic Behavior of Three-Phase Power Transformer Dynamic Model

This paper deals with the evaluation of various dynamic models for modeling of three-limb core power transformers. Due to the specific construction of such transformers, the magnetic fluxes in individual core limbs are interdependent. For applications, such as simulations and testing protection devices and algorithms, adequate prediction of inrush currents waveforms is crucial and indispensable. In this paper, various different dynamic models are tested where the limitations of standard models that are available in commercial program packages are pointed out. Two groups of models are evaluated separately: standard three-phase models composed of three-single phase transformer models and advanced three-phase models, where the topology of the magnetic circuit is considered. Furthermore, in both transformer model groups, three different material descriptions are evaluated, where linear, non-linear, and hysteretic material properties are considered. The comparison between measured and calculated waveforms of inrush currents is used to evaluate individual models. The comparison of evaluated models shows that standard dynamic models cannot provide proper waveforms of inrush currents regardless of which magnetically non-linear behavior of the iron core is considered. For adequate modeling of discussed transformers the consideration of the topology of the magnetic circuit is crucial.

Artificial Neural Network as Part of a Saturation-Level Detector Within the Transformer’s Magnetic Core

This paper deals with an algorithm for saturation-level detection within the iron core of a transformer, where the focus of this paper is on an artificial neural network (ANN)-based method. The iron core can be considered as saturated when the value of the ratio between the magnetically nonlinear characteristic flux linkage and the magnetomotive force decreases, thus dropping under the saturation-level value. However, the signal that represents the dynamic inductivity is contaminated with noise, which is substantially increased within the vicinities of the reversal points of the hysteresis, thus making reliable iron core saturation-level detection almost impossible. Therefore, some existing algorithms for iron core saturation detection based on dynamic inductivity criteria often fail when approaching the reversal point on the hysteresis loop. This paper presents an ANN-based method where the different structures of the ANNs are compared and evaluated.