Inaccurate phase connectivity information may cause several operational inefficiencies – for example, unbalanced phases that lead to significant energy losses and sharply reduced asset lifetimes. Traditional approaches to phase identification require either manual intervention or costly signal injection. These methods are usually infrequently performed. As a consequence, the phase identification can quickly become out-of-date. Using robust machine learning techniques, Itron’s Strategic Analytics group has developed algorithms to accurately classify meters according to their phase using voltage information readily available from AMI meters.

Itron’s phase identification is offered as a service, minimizing upfront cost. In addition, pilot programs are available for a limited number of feeders to allow the opportunity to evaluate the service’s accuracy and benefit to your utility.

Watch a recent webcast on our innovative phase identification technique here.

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Paige Schaefer
Sr Forecast Analyst - Itron
Paige Schaefer directs various web-based projects, including brown bag seminars, internet surveys, and other web-based projects and services. Schaefer manages Itron’s Energy Forecasting Group (EFG), which supports end-use data development, the Statistical End-use Approach (SAE) and coordinates their annual meeting for discussing end-use modeling and forecasting issues. In addition, Schaefer develops, manages and executes marketing campaigns for forecasting products and services and provides software support and documentation. She is responsible for project accounting and support, financial budgeting, accounting and invoicing. Schaefer received a B.S. in Business Administration from San Diego State University with an emphasis in Marketing.