Georgia Tech Research Corporation

Eaton Corporation

Eaton Intelligent Power Limited

Square D Company

University of Missouri

Clemson University

Ronald Gordon Harley

Thomas G Habetler

Bin Lu

Yi Yang

Liang Du

Santosh Kumar Sharma

Franklin Lambert

Mayura Arun Madane

Prachi Suresh Zambare

Paul LeBaron Springer, Iii

James E Mahaffey

Joy Mazumdar

Deepakraj M Divan

Peter J Theisen

Roy Stephen Colby

David J Gritter

Jiaqi Liang

Zhi Gao

Wei Zhou

Dawei He

Ganesh Kumar Venayagamoorthy

Thomas G Habelter

Rangarajan M Tallam

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 dynamic stochastic optimal power flow (DSOPF) control system is described for performing multi-objective optimal control capability in complex electrical power systems. The DSOPF system and method replaces the traditional adaptive critic designs (ACDs) and secondary voltage control, and provides a coordinated AC power flow control solution to the smart grid operation in an environment with high short-term uncertainty and variability. The DSOPF system and method is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The system and method dynamically drives a power system to its optimal operating point by continuously adjusting the steady-state set points sent by a traditional optimal power flow algorithm.

Method and system for dynamic stochastic optimal electric power flow control

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 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.

System and method employing a self-organizing map load feature database to identify electric load types of different electric loads

A system and method for nonintrusively determining electric motor efficiency includes a processor programed to, while the motor is in operation, determine a plurality of stator input currents, electrical input data, a rotor speed, a value of stator resistance, and an efficiency of the motor based on the determined rotor speed, the value of stator resistance, the plurality of stator input currents, and the electrical input data. The determination of the rotor speed is based on one of the input power and the plurality of stator input currents. The determination of the value of the stator resistance is based on at least one of a horsepower rating and a combination of the plurality of stator input currents and the electrical input data. The electrical input data includes at least one of an input power and a plurality of stator input voltages.

System and method to determine electric motor efficiency nonintrusively

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

System and method for bearing fault detection using stator current noise cancellation

A system and method for determining non-linear load harmonics impact on the voltage distortion at the point of common coupling is disclosed. Briefly described, one embodiment is a method comprising metering voltage on an electric power system; metering current on the electric power system; determining a predicted voltage based upon the metered current; comparing the predicted voltage with the metered voltage; and determining a harmonic voltage component using a plurality of weights determined when the predicted voltage converges with the metered voltage.

System and method for determining harmonic contributions from non-linear loads without disconnecting any load

An induction motor having a rotor and a stator is protected during running overloads by connecting the motor to an overload protection relay that can be tripped to interrupt power to the motor in the event of an overload, tracking the stator winding temperature of the motor during running overloads with a hybrid thermal model by online adjustment in the hybrid thermal model, and tripping the overload protection relay in response to a predetermined running overload condition represented by the tracked stator winding temperature. In one embodiment of the invention, the stator winding temperature is tracked by use of an online hybrid thermal model that uses the resistance of the rotor as an indicator of rotor temperature and thus of the thermal operating conditions of the motor. The hybrid thermal model incorporates rotor losses and heat transfer between the rotor and the stator, and approximates the thermal characteristics of the rotor and stator.

Electronic overload relay for mains-fed induction motors

A system and method for determining harmonics caused by non-linear loads are disclosed. Briefly described, one embodiment is a method comprising metering voltage on an electric power system; metering current on the electric power system; determining a predicted current based upon the metered voltage; comparing the predicted current with the metered current; and determining a harmonic current component using a plurality of weights determined when the predicted current converges with the metered current.

System and method for determining harmonic contributions from non-linear loads

A method and system for remotely inspecting the integrity of a structure. This can be performed by a method creating a vibratory response in the structure from a remote location and then measuring the vibratory response of the structure remotely. Alternatively, this can be performed by a system for remotely measuring the integrity of a structure using a vehicle and an artificial neural network, where the vehicle is equipped with a vibratory response device. The vibratory response can be produced by infrasonic and audio frequencies that can be produced by at least a vehicle, motor, or sound recording. The vibratory response can be measured with a laser vibrometer or an audio recording device.

Laser doppler vibrometer for remote assessment of structural components

A stator turn fault detection system and method capable of real-time detection of a stator turn fault in an electric motor is provided. The stator turn fault detector includes a feed forward neural network that when trained, using fundamental frequency sequence components of the voltage and current supplying the electric motor, will estimate a fundamental frequency sequence component of current indicative of a stator turn fault. A method for detecting a stator turn fault in an electric motor as well as a method for training a feed forward neural network for use with the stator turn fault detector is disclosed.

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Lawrenceville, GA, United States of America

Ronald Gordon Harley