An approach to diagnose incipient faults of power transformer using dissolved gas analysis of mineral oil by ratio methods using fuzzy logic

Abstract

Condition Monitoring plays a vigorous role in asset management strategy. Power Transformers are most important and critical component in electrical power transmission and distribution. To have a reliable electricity supply it is necessary to give considerable attention to maintenance of the transformer. Dissolved gas analysis (DGA) of transformer oil is one of the most effective power transformer condition assessment techniques to diagnose incipient faults. There are many interpretation methods for DGA diagnosis however all these methods rely on personnel expertise more than analytical calculation. As a result, various interpretation techniques do not necessarily indicate to the same result for the same oil sample. Furthermore, substantial number of DGA results fall outside the proposed codes of the existing ratio interpretation methods Furthermore, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this paper introduces a new fuzzy logic approach to reduce reliance on expert personnel and to aid in standardizing DGA interpretation methods. The approach relies on incorporating all existing DGA interpretation methods into one proficient model. DGA results of 100 oil samples that were collected from different transformers of different rating and different life period are used to create the model. Conventional DGA interpretation methods are used to analyse the Together DGA results to evaluate the consistency and accuracy of each interpretation methods. Results of this analysis were then used to develop the proposed fuzzy logic model.