Rockwell Automation Technologies Incorporated

Sean Charles Schmelzer

Linglai Li

David Bradly Berman

Phillip R Bush

Steven A Lombardi

Benfeng Tang

David Dale Brandt

Michael Lee Gasperi

Steven T Haensgen

Alex Laurence Nicoll

Philip John Kaufman

Noel Henderson

Kun Wei

Guolin Zhang

Richard Arthur Morgan

Fred M Discenzo

Xiaolong Zhang

Xuyuan Xiao

Various embodiments of the present technology generally relate to power topology discovery in industrial environments. More specifically, some embodiments relate to automatic power topology discovery for factories based on device data that is already recorded for other purposes. Systems and methods described herein may be used to generate an accurate electrical network topology by collecting power data from power devices that may provide real-time or recorded measurements, detecting power change events, and matching power change signatures over power events for the devices in order to calculate the likelihoods of possible topology assumptions. Power change event data is used to recursively update topology probabilities using the Bayesian formula until a system topology can be produced with satisfactory confidence.

Systems and methods for automatic power topology discovery

A system includes a plurality of power monitors that, in operation, monitor parameters of power in an automation system at points between loads and/or power sources. Each of the power monitors includes sensing circuitry to sense the power parameters, peer-to-peer communications circuitry to communicate with other power monitors via peer-to-peer communication, network communications circuitry to communicate with automation devices via a network, and functional circuits to perform analysis of monitored power parameters in a cooperative manner based upon the power parameters monitored by the respective power monitor and power parameters monitored by other power monitors communicated via peer-to-peer communication.

Systems and methods for monitoring power

An energy monitoring system includes a memory storing instructions to execute an energy modeling technique and processing circuitry for executing the instructions to operate the energy modeling technique. The energy modeling technique includes receiving energy data from a plurality of segments representative of one or more logical subgroups. The energy modeling technique includes categorizing the energy data of the logical subgroups into a plurality of segments. The energy modeling technique includes organizing the plurality of segments into a plurality of state-based hierarchical levels. The energy modeling technique includes calculating energy usage and factors associated with the plurality of state-based hierarchical levels via an energy model. The energy modeling technique includes outputting a visualization representative of the energy data corresponding to each of the segments to a monitoring and control system, resulting in a graphical representation accessible by a user-viewable screen.

State-based hierarchy energy modeling

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Milwaukee, WI, United States of America

Sean Charles Schmelzer