Leveraging Machine Learning for Phase Identification | Itron

Leveraging Machine Learning for Phase Identification

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 As a consequence, the phase identification can quickly become out-of-date.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.

Learn about Itron’s innovative phase identification technique presented by Jean Shelton.

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