Grid connectivity modeling uses data, algorithms to boost reliability
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 labor. 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without accurate and timely knowledge of meter to transformer and phase connectivity is like finding your way with an out-of-date map.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.”
With accurate knowledge of meter to transformer and phase connectivity, electric utilities can enable a balanced grid and achieve better outcomes from their applications, such as outage restoration, revenue protection, load balancing, system planning, voltage optimization and asset optimization. The technology is in the cloud and applies algorithms to determine the precise connectivity from every meter to its distribution transformer as well as the feeder phase supplying the power.
This helps utilities eliminate the substantial costs and risks associated with sending personnel into the field to trace the circuit of every meter to its distribution feeder as well achieve operational efficiencies and reduced truck rolls for unplanned outages.
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