Sandia’s electric power systems research team recently completed a toolset that improves visibility into the power grid by applying machine learning methods to customers’ home utility power meter data. The toolset provides the ability to determine the electrical phase of the power meter using the data produced by a neighborhood’s meters. As the power grid has evolved and changed, detailed power grid maps of which line goes where may become inaccurate over time. This toolset provides an automated way to update utility maps without the intensive man-hours and expense of field verification. Accurate utility maps are crucial for the continuing integration of solar panels and other power grid modernization goals.
The National Rural Electric Cooperative Association (NRECA) has included the phase identification tool in their open modeling framework software used by over 260 electric cooperatives and vendors throughout the United States. Incorporating these types of toolset algorithms into an established co-op utility tool allows for increased adoption in disadvantaged communities.
“Cooperatives deploy a lot of advanced metering systems to save time and cost when maintaining large rural distribution systems. This Sandia algorithm is an easy way to get more value and operational capabilities from these systems,” said David Pinney, NRECA’s Analytics Research Manager.
This new Sandia toolset will promote energy justice by providing access to key power grid modernization algorithms which help enable affordable solar energy integration in persistent poverty counties, 92% of which get their energy from electric cooperatives.
Sandia is also working with Eaton to include the toolset prototype in the CYME distribution software used by many utility companies in the U.S.
The toolset algorithm is also publicly available for utility and research applications on Sandia’s GitHub page.
