Conference Agenda

While holding several constructive similarities with power transformers, shunt reactors' thermal behavior differs significantly from transformers. Yet, the most common approach is to estimate the temperature distribution of shunt reactors using the same thermal model developed for power transformers. While adjusting some of the empirical coefficients of the model may lead to minimizing the deviations in the predicted temperatures, due to the exponential nature of the solid insulation life consumption, small deviations in the hottest spot temperature may represent several years or decades of difference in the expected lifespan.

A physics-based thermal model was included in the IEEE C57.91 in the 80s, traditionally referred to as the “Annex G” model. Rather than using time constants and exponential functions to predict the temperature gradients under different loading conditions, the model utilizes a model similar to a Runge-Kutta method, on a sequential calculation procedure based on an initial value, using small enough time steps. For each time step, equations are used to calculate the heat generated and dissipated by the different elements of the transformer, estimating the temperature variation for the average winding and the hotspot region.

Considering the differences in the variation of losses and heat dissipation as a function of the system voltage, the so-called “Annex G” model was adapted for shunt reactors. A comparison of the results with measurements and results from other models is presented in this article.

An inadequate isolation system (IS) design can result in transformer failures, which translate into severe economic repercussions due to the cost of repair or replacement, not to mention the interruption of the electric power supply.

The transformer insulation system is directly linked to its performance and useful life. Therefore, achieving the best possible IS design is a key objective. Appropriate design criteria can help accomplish this condition, and over the years, many criteria have been developed, such as the Weidmann® reference curves and safety margin calculation. This work focuses on the safety margins calculation of oil gaps, which are directly related to the probability of partial discharges in transformer oil.

The safety margins are directly related to the arrangement of the solid insulations immersed in the oil. In practice, safety margins must be properly estimated during the transformer design and optimization process. These margins must be adequate to guarantee that the equipment supports test levels and the dielectric stresses to which it will be subjected during its useful life.

A methodology supported by genetic optimization algorithms is developed to maximize safety margins in the arrangement of major insulation of a power transformer, considering restrictions such as the standard thickness of the barriers and typical sizes of oil ducts.

The work is divided into two main optimization stages. The first is the optimization of the major insulation between the windings, where the electrical stress is more critical. The second stage involves optimizing the insulation between the core and the low-voltage winding. The insulation stresses during applied potential and impulse tests were considered for each stage.

The configuration of the transformer's major insulation design was optimized through the genetic algorithm method (GA). The calculation of electrical stress was obtained through the finite element method (FEM).

The methodology was applied for the case study of a 20 MVA transformer of 115/13.8 kV of one Mexican manufacturer. The results show that optimal design presents a considerable improvement in the most critical safety margins compared to the original design of the transformer. The proposed developed methodology can be applied to any transformer immersed in oil.