Johanna Rosenlind

A method for evaluating the impact of electric vehicle charging on transformer hotspot temperature

The expected increasing market share of electric vehicles is a response to the combination of new technological developments, governmental financial control, and an attitude shift of residents to a more environmentally friendly lifestyle. The expected capacity required for charging, imposes changes in the load to the already existing components in the electric power grid. In order to continue managing these existing assets efficiently during this load change, it is important to evaluate the impact imposed by the battery charging.

Prerequisites for transformer lifetime modeling towards a better understanding

The transmission transformer represent a significant asset in the electrical network. The transformer is expensive to manufacture and it is costly to replace. The cost of the transformer replacement is approximately 4 million EURO which is larger than the average component replacement activity. Therefore it is desired to make the replacement both timely and smooth to reduce unnecessary costs. Life time modeling is a tool for achieving such cost efficient replacements.

Transformer hot-spot temperature estimation for short-time dynamic loading

On the transformer, the effect of thermal stress is aging of the solid insulation. Excessive thermal stress could damage the solid insulation. To avoid this, transformer condition monitoring systems could use thermal models to forecast the operating temperatures during dynamic loading. There are several thermal models of varying complexity, including the thermal models stipulated by the IEC and IEEE standards. In this paper, the thermal model which is referred to by IEC Std. 60076-7 as the differential method, is modified, so it accounts for the oil viscosity dependence on temperature. The model is validated using hot-spot temperature measurements from a 40-MVA transformer, OFAF-cooled, 21/115 kV which is located in the subarctic climate region.

Influence of Ambient Temperature on Transformer Overloading During Cold Load Pickup

This paper proposes a method to investigate the socioeconomical aspects of transformer overloading during a cold load pickup (CLPU) in residential areas. The method uses customer damage functions to estimate the cost for their power interruption and a deterioration model to estimate the cost for transformer wear due to the CLPU. A thermodynamic model is implemented to estimate the peak and the duration of cold residential load. A stochastic differential equation is used to capture the volatility of the load and to estimate the probability for transformer overloading. In a numerical example, an optimal cold load pickup for a two-area system is demonstrated where transformer overloading is allowed. In this example, an ambient temperature threshold is identified, where transformer overloading is socioeconomically beneficial.

Investigation of transformer top-oil temperature considering external factors

Standard estimation of top-oil temperature uses a thermal model related to load changes and variation of ambient temperature. Attempts have been done to improve the accuracy of top-oil temperature calculations by introducing internal properties into the model i.e. oil viscosity and winding resistance. The interest of this paper is to investigate the effect of external factors on top-oil temperature by looking into the weather, i.e. wind velocity. The results are compared with measurements on a 63MVA-ONAF 55/140 kV transformer unit, which is operated in ONAN cooling mode. The unit is located in subarctic climate, and it is equipped with a monitoring system and a weather station.

Modeling impact of cold load pickup on transformer aging using Ornstein-Uhlenbeck process

Thermostatically controlled devices, such as air conditioners, heaters, and heat pumps may cause cold load pickup (CLPU) problems after a prolonged blackout. This causes an increased load on the power components in the electrical grid. The result is unpredictable aging and increased risk of failure. Quantifying this risk is crucial for efficient asset management for cost-intensive components such as the transformer. This paper presents a new approach to model the loading profile of a CLPU using stochastic differential equations. The realization of the loading profile is used to determine the aging of a transformer. Two models for the deterioration of transformer solid insulation represent the loss of life due to the CLPU. A comparison between two models for the aging of the solid insulation in the transformer is made in a case study. Due to the stochastic behavior of the load, there is a probability for loading the transformer above the recommended ratings, and this probability is estimated with Monte Carlo simulations.