Stefano Talassi holds a bachelor’s degree in Electrical Engineering from the Politecnico of Milan and an executive MBA from Bocconi University, Italy. He has over 20 years of experience in the field of design, project management, testing, commissioning and after sales service in the transformer industry. He is not only responsible for designing but also for consultancy of industrial transformers. On normal days he designs industrial and furnace transformers and on other days he has the responsibility to commission and test the machinery, designing the unit from 10 MVA to 200-250 MVA, high voltage up to 400 kV. Currently, he is the technical director of an industrial transformer company in the North of Italy. He is jointly working as a business advisor for Seetalabs and Ronin AI Dashboard developments.

Let’s start the investigations!

Transformer health indexing is a sophisticated way to deduce critical information about a transformer’s condition assessment in order to make key decisions about replacement, repair, or even refurbishment. Click here for a dedicated read on the effective application of various health indexing strategies for transformers asset management.

Seetalabs’ developed the first-ever machine learning web-platform Ronin AI Dashboard, that is capable of showing trends of health index of up to 6 transformers in a single chart. This allows the asset manager to take a quick look on the performance of units along with the effectiveness of past maintenance actions.

What is an Electric Arc Furnace (EAF) transformer?

An Electric Arc Furnace (EAF) transformer is a device designed to increase or decrease the voltage of alternating current (AC) to make it suitable for application in steel-melting furnaces. They are the key to sustainable, resilient and efficient steel making technologies. Typically, a non-performing EAF transformer can disrupt power supply and have significant impacts on the overall production lines.

Often, condition monitoring goals of EAF transformer is to detect insulation breaches and potential for lifetime extension. Majority of these objectives are achieved by traditional condition monitoring tests including dissolved gas analysis (DGA) and other oil quality testing. Since the condition monitoring interests of EAF transformers converges well with our health indexing strategies, we can deliver good scope of application. In the present case, an EAF transformer with relevant data was reported to Stefano to pin-point issues with its performance and decide frequency of maintenance. Watch the video to gain more knowledge about the whole case-study.

This is only a foretaste of what is to come, and only the shadow of what is going to be..

Alan turing

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