Learn how to validate the quality of power system measurement data for more reliable assessments.
In this episode, David Gopp, Data Scientist and Digital Transformation Expert at OMICRON, discusses how pattern recognition ensures the optimal quality and validity of power system measurement data.
High-quality power system measurement data is essential for making reliable condition assessments of electrical equipment and predicting asset lifetime. David explains how artificial intelligence is effectively used to perform pattern recognition on measurement data to identify irregularities and to help users validate the accuracy of their measurements for more reliable assessments.
David also indicates how pattern recognition is currently applied to evaluate measurement data from power system testing performed on electrical equipment, such as power transformers, current transformers, rotating machines, and overhead lines. He tests the knowledge of listeners in a small quiz to guess the most relevant electrical asset for pattern recognition and reveals the answer towards the end of the episode with concrete examples.
Lastly, David describes which OMICRON power system testing solutions already utilize pattern recognition to help users work most efficiently with large amounts of measurement data.
Also of interest: Be sure to also listen to Energy Talks Episode 33, in which digital transformation experts David Gopp and Lukas Klingenschmid discuss the concepts of data readiness and data validation and how they play a key role in the digital transformation of the power industry:
Listen to episode 33 now
Listen to all Energy Talks episodes here: Podcast page
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