Title :
Modeling Transformers with Internal Incipient Faults
Author :
Wang, Huifang ; Butler, K. L.
Author_Institution :
Texas A& M University, College Station, TX
Abstract :
Incipient fault detection in transformers can provide early warning of electrical failure and could prevent catastrophic losses. To develop the transformer incipient fault detection technique, a transformer model is required, to simulate internal incipient faults. This paper presents a methodology to model internal incipient winding faults in distribution transformers. These models were implemented by combining deteriorating insulation models with an internal short-circuit fault model. The internal short-circuit fault model was developed using finite element analysis. The deteriorating insulation model, including an aging model and an arcing model connected in parallel, was developed based on the physical behavior of aging insulation and the arcing phenomena occurring when the insulation was severely damaged. The characteristics of the incipient faults from the simulation were compared with those from some potential experimental incipient fault cases. The comparison showed that the experimentally obtained characteristics of terminal behaviors of the faulted transformer were similar to the simulation results from the incipient fault models.
Keywords :
Aging; Artificial neural networks; Circuit faults; Circuit testing; Electrical fault detection; Expert systems; Field programmable gate arrays; Impulse testing; Power transformer insulation; Wavelet transforms; Distribution transformer; aging; arcing; finite element analysis; internal incipient winding fault; modeling;
Journal_Title :
Power Engineering Review, IEEE
DOI :
10.1109/MPER.2002.4312018