Abstract: In some embodiments, systems and methods described herein are directed to predicting asset failure and classifying failure modes in an electrical distribution system. In some embodiments, the system receives meter data from one or more electrical meters and associates the meter data with an asset to create asset data. In some embodiments, an AI model comprises a failure prediction model that determines a probability of failure based on the asset data, and a failure mode classification model that determines a predicted root cause classification based on the asset data. In some embodiments, the failure mode classification model receives the probability of failure from the failure prediction model for determining the predicted root cause classification. In some embodiments, the asset comprises a transformer in the electrical distribution system.

Abstract: In some embodiments, systems and methods described herein are directed to using smart meters to determine an operational status of an asset. In some embodiments, the asset is a transformer. In some embodiments, the system receives data from the smart meters such as voltage and associates the data with an asset feeding electricity to the smart meter. In some embodiments, the system includes a data analytics platform that can generate a failure probability prediction using the smart meter data. In some embodiments, the system includes an AI model configured to receive smart meter data, execute a decision analysis, and return a designation of whether the asset is at risk of failure or has failed.

Abstract: In some embodiments, systems and methods described herein are directed to using smart meters to determine an operational status of an asset. In some embodiments, the asset is a transformer. In some embodiments, the system receives data from the smart meters such as voltage and associates the data with an asset feeding electricity to the smart meter. In some embodiments, the system includes a data analytics platform that can generate a failure probability prediction using the smart meter data. In some embodiments, the system includes an AI model configured to receive smart meter data, execute a decision analysis, and return a designation of whether the asset is at risk of failure or has failed.

Abstract: Some embodiments include a system for metering an electrical grid comprising at least one processor executing instructions from a non-transitory computer-readable storage medium of an electrical grid fault detection system. In some embodiments of the system, the instructions cause a processor to calculate a prediction of whether power delivery to at least a portion of the electrical grid is functioning abnormally using voltage sensing devices coupled to at least one feeder, where one or more of the voltage sensing devices are responsive to a determination that the power delivery is functioning abnormally. Further in some embodiments, the determination includes the electrical grid fault detection system receiving at least one signal or voltage reading from the electrical grid based at least in part on a sensed or received voltage level or range of voltage level.