Effect of Modeling Non-Normality and Stochastic Dependence of Variables on Distribution Transformer Loss of Life Inference

Abstract

This paper presents a method for transformer loss-of-life inference by integrating stochastic dependence between non-normal transformer load and ambient temperature into analysis. The non-normally distributed variables are transformed to a common domain (i.e., the rank domain) by applying the cumulative density function transformation. In this domain, special functions, copulas, are used for modeling stochastic dependence between the variables. Extensive research data have been used to obtain quantitative results for realistic test cases of distribution transformers serving various types of low-voltage consumers. The results indicate that the accuracy of loss-of-life inference is very sensitive to normality and independence assumptions which are generally adopted in previous studies. It is demonstrated that such assumptions may lead to misleading results compared to the actual conditions. Thus, the proposed method, which is based on no restrictive assumption, emerges as a more accurate solution for transformer loss-of-life inference.

Simultaneous Network Reconfiguration and Wind Power Plants Participation in Day‐Ahead Electricity Market Considering Uncertainties

“…Therefore, the following method of calculating the equilibrium point in the presence of these uncertainties is presented: Since, in many studies, Monte-Carlo participant uncertainty modeling is done with distribution functions such as Weibull and Exponential, and these functions are far from their natural behavior, ECDF curves are used in this work to model the uncertainties. [30] To do this, first, based on the historical data of wind and uncertain customers' power, their ECDF curves are calculated. Then, by generating 1000 random numbers between 0 and 1, and using the resulted ECDF curves, 1000 wind power scenarios, and 1000 uncertain consumption scenarios are developed.…”

“…Based on the evaluations, this uncertainty modeling differs from their actual behavior. [ 30 ] Furthermore, this study aims only to increase the profit of wind farms and not to simultaneously increase the satisfaction of consumers and producers (profit). Moreover, no mechanism has been provided to smooth the fluctuations of electricity markets participants by contributing them in ancillary markets.…”

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

“…Overloading and unbalanced loading can distort the normal circulation of a current, thereby increasing the winding and oil temperatures, resulting in subsequent winding failure and insulation degradation. However, due to the economic aspects, winding temperatures are selected as the primary monitoring parameter [60], and other parameters like changes in current, aging, etc., are detected from the change in winding temperature [61]. For temperature monitoring, distributed temperature sensors are placed on the distribution transformer windings.…”

A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids

“…are usually selected as the most important parameters determining the transformer health [73]. The transformer aging index is also an important parameter, but this parameter can only be calculated through other parameters [74]. The estimation of transformer aging parameters is complex and non-deterministic because the heat transfer process is distributed over different surfaces in the winding and insulation structures and there may be measurement errors.…”