Insulation degradation, assessment of loss-of-life and optimization of transformer utilization in power distribution system

Although the incentive to load power transformers beyond their nameplate rating has always existed in the past, more recently utilities show more enthusiasm to fully utilize them to achieve greater profit in today's competitive electric energy market. One of the basic criterion, which limits the transformer loading capabilities, is the hottest-spot temperature of windings and the corresponding loss-of-life and the possibility of insulation failure. Transformer full load output limit is determined primarily by winding hottest-spot temperature. According to the IEEE Std. 057.91-1995, for the thermally upgraded paper as used today, it is limited to 110°C @ 30°C ambient temperature for a 65°C average winding temperature rise unit. Higher winding hottest-spot temperature causes degradation of the winding insulation. High temperatures decrease the mechanical strength and increase brittleness of fibrous insulation, increasing the potential of transformer failure. Gas bubbles may also form at elevated operating temperature, which may also cause the dielectric breakdown of transformer oil.; Under certain operating conditions, a transformer may be safely loaded beyond its nameplate rating. For every 1°C ambient temperature reduction (from standard 30°C) releases approximately 1% of overloading capability. The cold winter weather allows transformers to run at lower hottest-spot temperature, allowing for some overloading or saving of the insulation life. While in the summer, transformers run at higher ambient temperatures and possibly at higher than the rated hottest-spot temperature. The insulation degrades rapidly under these high temperatures and transformer life could be shortened substantially.; Utilities usually size and operate their transformers by matching the rating with the present demand and taking into consideration the future growth. Industry standard suggests transformer life expectancy to be between 20–30 years under “normal” operating conditions. In order to defer transformer replacement cost or cost of adding a second transformer under certain conditions, utilities may overload the transformer much beyond the nameplate rating and accept reduced life. The proposed research will address this very issue of economic decision based on the transformer remaining life-expectancy model and the future load growth. The probability tree structure is utilized to describe the future load growth pattern. The uncertainty of future load has been taken into account by this model. Together with probability tree model, the transformer thermal model has been employed to calculate service life of the transformer and determine when to replace an existing transformer with the new unit. The primary objective of this dissertation is to develop an optimization method to minimize the cost and select the proper transformer size for new applications and to optimize the replacement of transformer for an existing system and retrofit design. It is anticipated that the proposed method and the written computer program will help utilities in making decisions to minimize revenue requirements of the transformer over the long run to attain overall economic efficiency.