New tools for enhanced diagnostics of DGA data

Abstract

In the last decade there has been a significant change in the way transformers are viewed. Their importance together with their obvious value to the network has been enhanced and recognized, especially in light of the ageing fleet worldwide. At the other end of the spectrum, new transformers are now being designed and built to tighter tolerances as a result of competitive market conditions, with the knock-on effect that these “modern” devices do not appear to provide the same stability and longevity as those that were entering service in the 1970s and 1980s. Against this backdrop, the advent of transformer monitoring has emerged and continues to develop at a rapid pace. Although still considered an emerging component of asset management practice, online DGA is rapidly gaining acceptance and recognition as one of the most powerful tools in protection against asset failures. While other transformer monitoring technologies abound, many of them now online, such as partial discharge, these products collectively combine to enable the move to condition based monitoring of transformer assets. As online DGA monitors have evolved new products and technologies are reaching the market at an ever increasing rate. However, the quiet revolution is in the analysis of the data. As more and more monitors are installed, so the burden of data analysis becomes increasingly large. New ways of extracting value from this data required. One important approach is the use of Artificial Neural Networks (ANN) for DGA data analysis. Additionally, with the recognition that data from monitors must be easily transferred into meaningful information for the end-user, diagnostic tools, such as the Duval Triangle, have evolved where the addition of Triangles 4 and 5 brings significantly more value to previously mined data. The mute question in this paper relates to whether or not existing online monitoring hardware has sufficient accuracy and repeatability of measurement to be of use with these more advanced diagnostic tools.