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Tuesday, January 31, 2017

Machine learning for dynamic grid modeling boosts reliability

Itron's Idea Labs has developed a modeling technique that uses machine learning and existing voltage data to find out where all the meters are and dynamically balance the electricity grid.

The repair of distribution infrastructure following a power outage can sometimes lead to meters being reconnected to the wrong transformer or phase, which leads to issues with grid balancing. Itron Grid Connectivity helps utilities quickly and accurately identify meter-to-transformer and meter-to-phase connections using hourly interval voltage data and machine learning algorithms, without the need for expensive hardware or field labour. The technology was developed by Itron Idea Labs, an organization within Itron that accelerates business innovation.

 “Traditionally, verifying transformer and phase connectivity has required either visual tracing of overhead lines or sending and receiving electrical signals over the wire. With Itron Grid Connectivity, we are using existing voltage data to significantly improve the speed and accuracy of connectivity and helping utilities ensure high-performance grid operations and applications,” said Roberto Aiello, managing director of Itron Idea Labs. “Enabling smart grid applications without accurate and timely knowledge of meter to transformer and phase connectivity is like finding your way with an out-of-date map. Itron Grid Connectivity is like the GPS, accurately tracking meter locations in relation to the transformers that serve them.”

Read more at Dynamic grid connectivity modeling boosts reliability | EETE Power Management

By Nick Flaherty

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