Qualitrol Company LLC

Arin Technologies, Inc.

Aaron Acton

Paul Donegan

David Cole

Peter Glover

Peter Brett

Mark Diamond

Harish Balakrishnan

Pradyumna P Kulkarni

Mark Imgrund

Perry J Servedio

Systems, methods, and processor-readable storage media for AI continued learning in electrical power grid fault analysis use historical fault record data to generate a fault cause prediction model for predicting the cause of a fault, and modify the fault cause prediction model based on additional technician data received from power grid technicians. The systems disclosed herein additionally receive an indication of a fault which has occurred in a power grid, obtain a prediction of the cause of the fault by applying the indication of the fault to the fault cause prediction model, and cause the predicted cause of the fault to be remedied.

Systems and methods for AI continued learning in electrical power grid fault analysis

Systems, methods, and processor-readable storage media for AI-assisted electrical power grid fault analysis predict the cause of a fault and cause the fault to be remedied by receiving an indication that a first fault has occurred, identifying a plurality of additional fault records associated with the first fault, obtaining a first prediction of the cause of the fault based on the first fault record and the plurality of fault records by applying the fault records to a machine learning model, obtaining a second prediction of the cause of the fault by applying the fault records to a rules-based model, and obtaining a final prediction of the cause of the first fault based on the first prediction and the second prediction. The final prediction of the cause of the first fault is used to cause the predicted cause of the first fault to be remedied.

Systems and methods for ai-assisted electrical power grid fault analysis

One embodiment provides a method, including: receiving, from at least one transmitter coupled to an object, a transmission signal, wherein the transmission signal is received at a subset of a plurality of receivers, wherein the plurality of receivers are connected together through a network and have a synchronized clock; identifying, for each of the subset of the plurality of receivers, a time of receipt of the transmission signal and marking the transmission signal with a timestamp corresponding to the time of receipt; determining, using the marked transmission signals, a relative position of the object relative to the receivers; and identifying a location of the object within an environment by correlating the determined relative position of the object to a map of the environment. Other aspects are described and claimed.

Object tracking using receivers

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Pittsburgh, PA, United States of America

Aaron Acton