Eneryield is forming a collaboration with the power quality and smart grid specialists Unipower. The purpose is to implement Eneryield’s machine learning methods for intelligent analytics of power quality in Unipower’s supervision system.
Eneryield’s technology, developed by the Chalmers University of Technology researchers Ebrahim Balouji and Karl Bäckström, are capable of automatically classifying and suggest the direction of disturbances with a very high accuracy. The brand new functionality of finding the root-cause of a disturbance will also be enabled, making it easier to decide who is responsible for it or to mitigate it.
Unipower’s product line ranges from portable PQ analyzers to fully integrated and automated Power Quality Management systems for continuous supervision of the energy supply, used by leading power utilities and industry actors. They lead the market in Scandinavia and are present in more than 50 countries around the globe. Unipower is part of the Sdiptech technology group, which is listed on First North and has sales of approximately 1 billion SEK in total.
“Both parties add a unique contribution to the collaboration.”
By being able to train and use Eneryield’s machine learning methods on the large amount of power quality data that Unipower has access to, both parties add a unique contribution to the collaboration. This combined strength creates opportunities to launch the next generation of data-driven and self-learning power quality supervision, which is necessary to accelerate the transformation towards a more electrified society.
Unipower will initially offer the functionality as a report that can be generated in their supervision system PQ Secure.