Eaton Corporation

Eaton Intelligent Power Limited

Georgia Tech Research Corporation

The United States of America as Represented by the Department of Energy

Yi Yang

Liang Du

Mayura Arun Madane

Prachi Suresh Zambare

Bin Lu

Santosh Kumar Sharma

Ronald Gordon Harley

Dawei He

Charles John Luebke

Xin Zhou

Thomas G Habetler

Vijay Bhavaraju

Thomas J Schoepf

Mikhail Goykhman

Qiang Fu

David W Ganger

Abrez Mondal

Souvik Chandra

Electrically connecting a first node of a first power grid to a second node of a second power grid includes: determining a phase angle of at least one phase of an AC voltage at the first node in the first power grid; determining a phase angle of at least one phase of an AC voltage at the second node in the second power grid; determining a phase angle metric based on comparing the phase angle of the AC voltage in the first power grid to the phase angle of the AC voltage in the second power grid; comparing the phase angle metric to a phase angle threshold; and if the phase angle metric is equal to or exceeds the phase angle threshold, controlling a dispatchable energy source in the first power grid in a P-Q control mode to adjust the phase angle of at least one phase of the AC voltage at the first node.

Synchronization of electrical power grids

A method of monitoring an electrical system having a plurality of electrical loads may include operating the plurality of electrical loads, obtaining a combined power profile for the plurality of electrical loads, decomposing the combined power profile into individual electrical load profiles from the combined power profile, and controlling operation of one or more electrical loads of the plurality of electrical devices according to the individual electrical load profile of the one or more electrical loads. Decomposing the combined power profile may include applying multi-dimensional event decomposition to the combined power profile to identify and disaggregate the combined power profile into the individual electrical load profiles. Decomposing the combined power profile may include detecting power consumption events of the plurality of electrical loads. Decomposing the combined power profile may include classifying the detected power consumption events.

System and method for monitoring and controlling electrical loads

A system characterizes and identifies one of a plurality of different operating modes of a number of electric loads. The system includes a processor; a voltage sensor providing a voltage signal for one of the electric loads to the processor; a current sensor providing a current signal for the one electric load to the processor; and a routine executed by the processor and structured to characterize the different operating modes using steady state and voltage-current trajectory features determined from the voltage and current signals, and to identify a particular one of the different operating modes based on a plurality of operating mode membership functions of the steady state and voltage-current trajectory features.

System and method to characterize and identify operating modes of electric loads

A method identifies electric load types of a plurality of different electric loads. The method includes providing a support vector machine load feature database of a plurality of different electric load types; sensing a voltage signal and a current signal for each of the different electric loads; determining a load feature vector including at least six steady-state features with a processor from the sensed voltage signal and the sensed current signal; and identifying one of the different electric load types by relating the load feature vector including the at least six steady-state features to the support vector machine load feature database.

System and method for electric load identification and classification employing support vector machine

A system disaggregates and estimates power consumption of electric loads powered by a single electrical outlet. The system includes a processor having a routine; a current sensor cooperating with the processor to measure samples for one line cycle of an aggregated current waveform for the electric loads powered by the single electrical outlet; and a voltage sensor cooperating with the processor to measure samples for the one line cycle of a voltage waveform for the electric loads powered by the single electrical outlet. The processor routine transfers the measured samples for the one line cycle of the aggregated current waveform and the voltage waveform into an aggregated voltage-current trajectory for the single electrical outlet, and provides an instantaneous decomposition of power consumption for a plurality of different categories of the electric loads from the aggregated voltage-current trajectory for the one line cycle.

System and method for instantaneous power decomposition and estimation

A load power device includes a power input, at least one power output for at least one load, a plurality of sensors structured to sense voltage and current at the at least one power output, and a processor. The processor provides: (a) load identification based upon the sensed voltage and current, and (b) load control and management based upon the load identification.

Load power device, system and method of load control and management employing load identification

A load power device includes a power input; at least one power output for at least one load; and a plurality of sensors structured to sense voltage and current at the at least one power output. A processor is structured to provide real-time execution of: (a) a plurality of load identification algorithms, and (b) event detection and operating mode detection for the at least one load.

Load power device and system for real-time execution of hierarchical load identification algorithms

A method of protecting a power distribution system from a fault on a feed conductor thereof, wherein an HFAC signal is provided to the feed conductor from a location downstream of the feed conductor in the power distribution system. The method includes, in a module located upstream of the feed conductor in the power distribution system, determining an HFAC signal magnitude in the module, the module including a circuit interrupter, and controlling operation of the circuit interrupter based on the determined magnitude.

Fault detection and zone location system

A system for different electric loads includes sensors structured to sense voltage and current signals for each of the different electric loads; a hierarchical load feature database having a plurality of layers, with one of the layers including a plurality of different load categories; and a processor. The processor acquires voltage and current waveforms from the sensors for a corresponding one of the different electric loads; maps a voltage-current trajectory to a grid including a plurality of cells, each of which is assigned a binary value of zero or one; extracts a plurality of different features from the mapped grid of cells as a graphical signature of the corresponding one of the different electric loads; derives a category of the corresponding one of the different electric loads from the database; and identifies one of a plurality of different electric load types for the corresponding one of the different electric loads.

Method and system employing graphical electric load categorization to identify one of a plurality of different electric load types

A system is for a plurality of different electric load types. The system includes a plurality of sensors structured to sense a voltage signal and a current signal for each of the different electric loads; and a processor. The processor acquires a voltage and current waveform from the sensors for a corresponding one of the different electric load types; calculates a power or current RMS profile of the waveform; quantizes the power or current RMS profile into a set of quantized state-values; evaluates a state-duration for each of the quantized state-values; evaluates a plurality of state-types based on the power or current RMS profile and the quantized state-values; generates a state-sequence that describes a corresponding finite state machine model of a generalized load start-up or transient profile for the corresponding electric load type; and identifies the corresponding electric load type.

Method and system employing finite state machine modeling to identify one of a plurality of different electric load types

A method identifies electric load types of a plurality of different electric loads. The method includes providing a load feature database of a plurality of different electric load types, each of the different electric load types including a first load feature vector having at least four different load features; sensing a voltage signal and a current signal for each of the different electric loads; determining a second load feature vector comprising at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the different electric loads; and identifying by a processor one of the different electric load types by determining a minimum distance of the second load feature vector to the first load feature vector of the different electric load types of the load feature database.

System and method employing a minimum distance and a load feature database to identify electric load types of different electric loads

A method identifies electric load types of a plurality of different electric loads. The method includes providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of the load types corresponding to a number of the neurons; employing a weight vector for each of the neurons; sensing a voltage signal and a current signal for each of the loads; determining a load feature vector including at least four different load features from the sensed voltage signal and the sensed current signal for a corresponding one of the loads; and identifying by a processor one of the load types by relating the load feature vector to the neurons of the database by identifying the weight vector of one of the neurons corresponding to the one of the load types that is a minimal distance to the load feature vector.

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Milwaukee, WI, United States of America

Yi Yang