Driving reliability with machine learning and improving operation by digitalization of medium power transformers release_ts24732utvd35lbodlb4ndnubm

Abstract

Driving reliability with machine learning and improving operation by digitalization of medium power transformersFor the new challenges of energy supply a new generation of analytical capabilities with the ability to provide deeper insights is mandatory.Machine learning in combination with artificial intelligence can become the new methodological approach for the diagnostics of equipment in the distribution network. In that way machine learning is based on pattern and evaluation of statistical values and herewith it generates knowledge from experience. Such a system combines monitoring, intelligent sensors, accessories needed for communication and field devices with intelligent software solutions.Users want more and more intelligence and usability in the programs which support human decision-making. The advent of new methods of data modelling, with interpretation using statistical analysis, expert rule systems and the combination of machine learning systems with artificial intelligence is now moving from research stage to practical field implementation.Machine Learning is based on pattern and evaluation of statistical values, and herewith it generates knowledge from experienceTherefore, the paper wants to introduce an advanced method for analysis, visualization and interpretation of vibro-acoustic online measurements of on-load tap-changers, particularly in relation to their application with medium power transformers in service.For the new, intelligent transformer monitoring systems based on integrated smart modules, one of the goals was to develop self-learning Systems.
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