Life characterization of power distribution transformers using clustering techniques
The current development of the smart grids has considerably increased the amount of research studies around new exploitation paradigms focused on the electrical distribution systems. One of the key elements of electrical distribution networks is the distribution transformer that supports the load that has to feed the consumer needs. This paper aims at characterizing the life of the distribution transformers using clustering techniques. This will make it possible to focus the attention of the Distribution System Operator on particular groups of distribution transformers reducing the amount of information to be analyzed. Also this classification combined with the study of stress indicators for each distribution transformer can be used to complement the criteria used for the network planning and operation.
How to Cite
condition monitoring, distribution power transformer, clustering, life indicators, neural networks
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.