A Master of Science Thesis in Electrical Engineering Submitted by Refat Atef Ghunem Entitled, "Transformer Condition Assessment Using Artificial Intelligence," May 2010. Available are both Soft and Hard Copies of the Thesis.
As a result of the deregulations in the power system networks, utilities have been competing to optimize their operational costs and enhance the reliability of their electrical infrastructure. So, the importance of implementing effective asset management plans that improve the life cycle management of the electrical equipment is highlighted. From an asset management point of view, commonly practiced maintenance strategies are considered to have large portions of redundant costs. Therefore, applying cost-effective, reliable and conditionally-based maintenance policy is a priority. Having certain and comprehensive condition assessment of the electrical equipment supports the selection of the appropriate maintenance plan. Power transformers are the most costly electrical infrastructures; hence, condition assessment of power transformers is a necessary task. It is a well accepted fact that the remnant useful life of the transformer paper insulation determines its useful operational life. Thus, reliable and economic transformer's insulation condition monitoring and diagnostic techniques are necessary to conduct a comprehensive and efficient transformer condition assessment. In this dissertation, artificial neural network is utilized as a modeling tool to predict transformer oil parameters. Accordingly, the diagnosis efficiency of several transformer condition monitoring techniques are enhanced and both corrective maintenance and end-of-life assessment costs are reduced. The research is focused in predicting parameters able to diagnose both transformer insulating oil and its paper insulation condition. Transformer insulation resistance parameter is used as an input for an artificial neural network prediction-based model to estimate transformer oil breakdown voltage, water content and dissolved gases. Moreover, furan content in transformer oil is predicted using artificial neural network with step-wise regression as a feature extraction tool. An effective prediction model of oil breakdown voltage, water content, dissolved gases and carbon dioxide to carbon monoxide ratio with prediction accuracies of 97%, 85%, 88% and 91% respectively is achieved. The excellent prediction accuracies achieved reduces inspections' time of unplanned outages. Furthermore, Oil quality parameters and dissolved gases are verified to be statistically significant inputs for the correlation with transformer oil furan content. The correlation is confirmed with a prediction accuracy of 90%. By achieving such accuracy, assessing the transformer's solid insulation and ultimately verifying its useful remaining life is approached.