Power Transformer Monitoring and Diagnosis using Transformer Explorer
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Power transformers are one of the most expensive and vital components in the power system. A sudden failure could be a very costly process for both the transformer owner and the society. Several monitoring and diagnostic techniques have been developed over the last decades to detect incipient transformer problems at an early stage, so that planned outages for maintenance and reparation can be carried out in time. However, the majority of these methods are only secondary indicators which do not address the transformers fundamental function: to transfer electric energy between different voltage levels with turn ratio, short-circuit impedance and power loss within acceptable limits.
Transformer Explorer is a concept developed by ABB which utilizes ordinary current and voltage signals available in the substation to extract transformer fundamental parameters such as: turn ratio, magnetizing current, impedance and power loss, which has significant diagnostic value. By estimating these parameters the method should be able to detect a number of problems related to the windings and the magnetic circuit of the transformer. Transformer Explorer is expected to find it's application in two different versions, either as an permanent on-line monitoring and diagnostic tool or as a short-time version for temporary measurements.
The thesis could be divided into three main parts. The first one focusing on a quantitative study trying to answer questions regarding the concepts feasibility when the temporary version is used. The second part is about optimizing and improving the procedure by which the fundamental parameters are estimated. In the last part, a new method for reducing the impact of errors introduced by the acquisition system on the estimated power loss is proposed. All the investigations related to the three topics covered in this thesis showed interesting and promising results.
Place, publisher, year, edition, pages
2016. , 68 p.
UPTEC E, ISSN 1654-7616 ; 16003
Electrical Engineering, Electronic Engineering, Information Engineering Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-280958OAI: oai:DiVA.org:uu-280958DiVA: diva2:912439
Master Programme in Electrical Engineering
Bergkvist, Mikael, UniversitetslektorThomas, Karin, Universitetslektor