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

Abstract

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 transformer's 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.