An Experimental Test Set-Up Design for Acquiring Creep Curve of the Spacer Between the Winding Turns of Power Transformers

Due to the rapid increase in energy demand, the equipment used in the transmission and distribution network is subjected to thermal overloading. Additionally, electrical stress due to high voltage and contaminations from environmental/structural factors lead to the life of insulating material to decrease rapidly. Among the equipment used in power systems, power transformers are one of the most affected ones from these undesired and distorted conditions. Particularly for oil type transformers, it is quite difficult to provide energy continuity due to failures caused by insulation problems, serious economic losses occur and more importantly, there might be cases where life loss is experienced. In order to avoid from such this cases, the electrical and mechanical properties of the insulation material should be analyzed correctly and the behaviors against different stresses should also be examined. In this context, it is crucial to investigate the problem of insulation between windings, which encountered quite often and has an important place among transformer failure reasons. Along with creeping of spacer part between the windings, the windings get closer to each other and by breaking down of the insulation material around the conductor due to overheating cause failure. In this study, a test system is designed and realized to determine the creep characteristic of spacer component, which is used to provide oil flow between the windings and for easier cooling, under different thermal and mechanical stresses. In this designed test set up, the spacer is placed in transformer oil and exposed to both mechanical and thermal stresses whose amplitudes can be controlled. By this way, it is aimed to investigate the effects of various thermal and mechanical stresses on the creeping of the spacer part. The results of the tests show that the creep curves will help to make the power transformers’ aging calculations and failure prediction algorithms more accurate.