The University of Alabama

Shuhui Li

Xingang Fu

Weizhen Dong

Michael Howard Fairbank

Yang Sun

Tim A Haskew

Hoyun Won

Timothy Austin Haskew

Donald Wunsch

Ishan Jaithwa

Yang Xiao

Raed Suftah

Ming Sun

Malek Ramezani

Dong Zhang

Eduardo Alonso

Yixiang Gao

A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special technique is developed to generate neural network training data, that is particularly suitable and favorable, to develop a high-performance neural network-based IPM torque control system, and the impact of variable motor parameters is embedded into the neural network system development and training. The provided method can achieve a fast and accurate current reference generation with a simple neural network structure, for optimal torque control of an IPM motor. The method can handle the MTPA, MTPV, and flux-weakening operation considering physical motor constraints.

Neural-network based MTPA, flux-weakening and MTPV for IPM motor control and drives

Described herein is a method and system for controlling an interior-mounted permanent magnet (IPM) alternating-current (AC) electrical machine utilizing a space vector pulse-width modulated (SVPWM) converter operably connected between an electrical power source and the IPM AC electrical machine comprising three neural networks (NNs), including a controller NN operably connected to the SVPWM converter, a parameter estimator NN, and a flux-weakening and MTPA NN.

Systems, methods and devices for neural network control for IPM motor drives

An example method for controlling a DC/DC converter or a standalone DC microgrid comprises an artificial neural network (ANN) based control method integrated with droop control. The ANN is trained to implement optimal control based on approximate dynamic programming. In one example, Levenberg-Marquardt (LM) algorithm is used to train the ANN, where the Jacobian matrix needed by LM algorithm is calculated via a Forward Accumulation Through Time algorithm. The ANN performance is evaluated by using power converter average and switching models. Performance evaluation shows that a well-trained ANN controller has a strong ability to maintain voltage stability of a standalone DC microgrid and manage the power sharing among the parallel distributed generation units. Even in dynamic and power converter switching environments, the ANN controller shows an ability to trace rapidly changing reference commands and tolerate system disturbances, and operate the DC/DC converter or the microgrid in standalone conditions.

Systems, methods and devices for control of DC/DC converters and a standalone DC microgrid using artificial neural networks

A system is described herein for controlling an inverter comprising an artificial neural network (ANN) and a space vector pulse-width modulation converter. The ANN comprises an input layer, plurality of hidden layers, an output layer, and a processor. The ANN receives a plurality of input signals and produces output signals that control a SVPWM converter. The ANN is trained to minimize a cost function and to implement optimal control based on ADP. Further, the ANN can be configured to handle rated current and PWM saturation constraints in providing volt/VAR control functions. Finally, the ANN uses averaged feedback signals instead of instantaneous feedback signals to improve volt/VAR control performance of parallel inverters. Performance evaluation shows that an ANN controller has a strong ability to maintain volt and VAR control at the grid edge and prevent fighting between inverters thereby allowing an inverter to work effectively in parallel with other ANN-controlled inverters.

Artificial neural network control system for autonomous volt/VAR control of inverter in interfaced units

A dc/dc buck converter controller comprises an artificial neural network (ANN) controller comprising an input layer and an output layer. The input layer receives an error value of a dc/dc buck converter and an integral of the error value. The output layer produces an output error voltage. A pulse-width-modulation (PWM) sliding mode controller (SMC) is configured to receive the output error voltage and produce a control action voltage by multiplying the output error voltage with a PWM gain. A drive circuit is configured to receive the control action voltage and provide a drive voltage to an input switch of the dc/dc buck converter. A PI control block is configured to modify a reference output voltage based on a maximum current constraint input. A locking circuit is configured to maintains the output error voltage at the saturation limit of the PWM SMC to manage a maximum duty cycle constraint.

Control of a buck dc/dc converter using approximate dynamic programming and artificial neural networks

Described herein is a neural network-based vector control method for the induction motor. The disclosure includes an approach to implement optimal vector control for an induction motor by using an NN; a NN controller to substitute two decoupled proportional-integral (PI) controllers in current loop; and, a mechanism to train the NN controller by using a Levenberg-Marquardt (LM)+forward accumulation through time (FATT) algorithm.

Systems, methods and devices for vector control of induction machines using artificial neural networks

Described herein is an approximate dynamic programming (ADP) vector controller for control of a permanent magnet (PM) motor. The ADP controller is developed using the full dynamic equation of a PM motor and implemented using an artificial neural network (ANN). A feedforward control strategy is integrated with the ANN-based ADP controller to enhance the stability and transient performance of the ADP controller in both linear and over modulation regions. Simulation and hardware experiments demonstrate that the proposed ANN-based ADP controller can track large reference changes with high efficiency and reliability for PM motor operation in linear and over modulation regions.

Systems, methods and devices for approximate dynamic programming vector controllers for operation of IPM motors in linear and over modulation regions

An example system for controlling a grid-connected energy source using a neural network is described herein. The example system can include a grid-connected converter ('GCC') operably coupled between an electrical grid and an energy source, a n-order grid filter (e.g., where n is an integer greater than or equal to 2) operably coupled between the electrical grid and the GCC, and a nested-loop controller. The nested-loop controller can have inner and outer control loops and can be operably coupled to the GCC. A d-axis loop can control real power, and a q-axis loop can control reactive power. Additionally, the inner control loop can include a neural network that is configured to optimize dq-control voltages for controlling the GCC. The neural network can account for circuit dynamics of the n-order grid filter while optimizing the dq-control voltages.

Systems and methods for providing vector control of a grid connected converter with a resonant circuit grid filter

According to various implementations, a demand response (DR) strategy system is described that can effectively model the HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days. This modeled energy consumption may be used with day-ahead energy pricing and the weather forecast for the location of the house to develop a DR strategy that is more effective than prior DR strategies. In addition, a computational experiment system is described that generates DR strategies based on various energy consumption models and simulated energy usage data for the house and compares the cost effectiveness and energy usage of the generated DR strategies.

Systems and methods for modeling energy consumption and creating demand response strategies using learning-based approaches

An example method for controlling an AC electrical machine can include providing a PWM converter operably connected between an electrical power source and the AC electrical machine and providing a neural network vector control system operably connected to the PWM converter. The control system can include a current-loop neural network configured to receive a plurality of inputs. The current-loop neural network can be configured to optimize the compensating dq-control voltage. The inputs can be d- and q-axis currents, d- and q-axis error signals, predicted d- and q-axis current signals, and a feedback compensating dq-control voltage. The d- and q-axis error signals can be a difference between the d- and q-axis currents and reference d- and q-axis currents, respectively. The method can further include outputting a compensating dq-control voltage from the current-loop neural network and controlling the PWM converter using the compensating dq-control voltage.

Systems, methods and devices for vector control of permanent magnet synchronous machines using artificial neural networks

Aspects of the disclosure relate to a control approach that utilizes a direct-current-based d-q vector control technology for variable-speed PMSG wind turbines based on full voltage source PWM converters. The control approach can be based on a nonlinear programming configuration for attaining a desired performance of PMSG wind turbine under operation constraints. The control approach can comprise a PMSG control unit that exploits fuzzy, adaptive, and PID control technologies in an optimal or nearly optimal control configuration. The control approach provides a smart wind turbine control technology that can be based on virtual lookup tables for effective PMSG power extraction.

Control of a permanent magnet synchronous generator wind turbine

Systems and methods for performing vector control of grid-connected power electronic converters using a neural network are described herein. Optionally, the grid-connected power electronic converters can be used in renewable and electric power system applications. In order to improve performance and stability under disturbance conditions, integrals of error signals can be introduced as inputs to the neural network. Alternatively or additionally, grid disturbance voltage can be introduced to an output of a trained neural network.

Vector control of grid-connected power electronic converter using artificial neural networks

Embodiments according to the present invention provide methods and a system for a control approach that effectively maintains the DC link voltage at a constant set value under variable system conditions and keeps the converter operating with an optimal power factor range while limiting power quality effects and phase balance.

Intelligent power converter control for grid integration of renewable energies

Embodiments according to the present invention provide methods and a system for a control approach that effectively maintains the DC link voltage at a constant set value under variable system conditions and keeps the converter operating within an optimal power factor range.

Growing community of inventors

Northport, AL, United States of America

Shuhui Li