Da Zhang

Development of a Unified Design, Test, and Research Platform for Wind Energy Systems Based on Hardware-in-the-Loop Real-Time Simulation

Traditionally, offline modeling and simulation has been the tool of choice for improving wind energy system control strategies and their utility system integration. This paper exploits how a newly established real-time hardware-in-the-loop (HIL) test facility, which is designed for testing all-electric ship propulsion systems, can be utilized for wind energy research. The test site uses two 2.5-MW/220-rpm dynamometers and a 5-MW variable voltage and frequency converter to emulate a realistic dynamic environment, both mechanically and electrically. The facility is controlled by a digital real-time electric power system simulator that is capable of simulating electrical networks and control systems of substantial complexity, typically with a 50-mus time step. Substantial input/output allows the feedback of measured quantities into the simulation. A 15-kW mock-up motor-generator set is used to demonstrate some critical aspects of the concept including the implementation of a proposed neural-network-based sensorless maximum wind energy capture control. From the dynamic test results presented, it is concluded that the proposed system shows great potential for the development of a unified wind energy design, test, and research platform

A Stochastic-Based FPGA Controller for an Induction Motor Drive With Integrated Neural Network Algorithms

This paper applies stochastic theory to the design and implementation of field-oriented control of an induction motor drive using a single field-programmable gate array (FPGA) device and integrated neural network (NN) algorithms. Normally, NNs are characterized as heavily parallel calculation algorithms that employ enormous computational resources and are less useful for economical digital hardware implementations. A stochastic NN structure is proposed in this paper for an FPGA implementation of a feedforward NN to estimate the feedback signals in an induction motor drive. The stochastic arithmetic simplifies the computational elements of the NN and significantly reduces the number of logic gates required for the proposed NN estimator. A new stochastic proportional-integral speed controller is also developed with antiwindup functionality. Compared with conventional digital controls for motor drives, the proposed stochastic-based algorithm enhances the arithmetic operations of the FPGA, saves digital resources, and permits the NN algorithms and classical control algorithms to be easily interfaced and implemented on a single low-complexity, inexpensive FPGA. The algorithm has been realized using a single FPGA XC3S400 from Xilinx, Inc. A hardware-in-the-loop (HIL) test platform using a Real Time Digital Simulator is built in the laboratory. The HIL experimental results are provided to verify the proposed FPGA controller.

Development of a unified design, test, and research platform for wind energy systems based on hardware-in-the-loop real time simulation

Traditionally, off-line modeling and simulation has been the tool of choice for improving wind energy system control strategies their utility system integration. This paper exploits how a newly established real-time hardware-in-loop (HIL) test facility, designed for testing all-electric ship propulsion systems, can be utilized for wind energy research. The test site uses two 2.5 MW/220 rpm dynamometers and a 5 MW variable voltage and frequency converter to emulate a realistic dynamic environment, both mechanically and electrically. The facility is controlled by a digital real-time electric power system simulator (RTDS), capable of simulating electrical networks and control systems of substantial complexity, typically with 50 microseconds time step. Substantial I/O allows the feedback of measured quantities into the simulation. A 15 kW mock-up motor-generator set is used to demonstrate some critical aspects of the concept. From dynamic test results presented it is concluded that the proposed system shows great potential for development of a one-of-a-kind wind energy research platform.

Digital Anti-Windup PI Controllers for Variable-Speed Motor Drives Using FPGA and Stochastic Theory

The windup phenomenon occurs when the output of a proportional-integral (PI) controller is saturated, which results in performance degradation or even instability. In this letter, three new anti-windup algorithms are proposed for a digital PI-speed controller to improve the control performance of variable-speed motor drives. These designs are implemented in a field programmable gate array (FPGA) device and stochastic theory is employed to enhance the computational capability of FPGA. Compared with conventional digital anti-windup techniques, the proposed methods offer several advantages: large dynamic range, easy digital design, minimal scaling of digital circuits, reconfigurability, and direct hardware implementation, while maintaining high control performance. The developed controllers are applied to the speed control of a field-oriented controlled induction motor drive using a hardware-in-the-loop test bench. The improved speed responses confirm the effectiveness of the proposed anti-windup schemes

A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems

This letter presents a reconfigurable hardware implementation of feed-forward neural networks using stochastic techniques. The design is based on the stochastic computation theory to approximate the nonlinear sigmoid activation functions with reduced digital logic resources. The large parallel neural network structure is then implemented on a reconfigurable field-programmable gate array (FPGA) device with high fault tolerance capability. The method is applied to a neural-network based wind-speed sensorless control of a small wind turbine system. The experimental results confirmed the validity of the developed stochastic FPGA implementation. The general design method can be extended to include other power electronics applications with different feed-forward neural network structures

Improved Performance and Control of Hybrid Cascaded H-bridge Inverter for Utility Interactive Renewable Energy Applications

This paper presents capacitor voltage balance control methods for hybrid cascaded H-bridge inverter with single DC source to remove higher order of harmonics, the power quality is therefore improved with low THD of output power for utility interactive inverters. In addition, a new control strategy based on voltage frequency regulation is proposed in this paper to achieve faster and smoother transition from grid-connected mode to stand-alone mode. For sensitive and mission critical local loads, a seamless transition between two operation modes is very valuable, especially for the transition from grid-tied mode to stand-alone mode in the case of utility fault. The proposed method not only implement fast transition, the load voltage waveform also becomes continuous during transients. The proposed control methods can be easily applied for multi-bus micro-grid control system with least modification. The simulation and experimental results of single-phase utility interactive inverter confirmed the validity of proposed control algorithms.

A multilevel power conditioning system for superconductive magnetic energy storage

The introduction of multilevel converters makes possible the use of pulse width modulation (PWM) converters and fast switching medium power devices like insulated gate bipolar transistors (IGBTs) for high-voltage, high-power applications, such as flexible ac transmission systems (FACTS). This paper proposes a five-level voltage source inverter (VSI)-chopper for a superconductive magnetic energy storage (SMES) power conditioning system (PCS). The circuit topology and operation principles are presented. The advantages of the proposed power conditioning system have been investigated and compared with present PCS including a traditional transformer coupled multi-pulse VSI-chopper topology. The validity of the system is verified by computer simulation.

A low cost digital implementation of feed-forward neural networks applied to a variable-speed wind turbine system

This paper presents a low cost hardware implementation of feed-forward neural networks using VHDL techniques. The design is based on the stochastic theory to achieve the nonlinear sigmoid function with reduced digital logic resources. The large parallel neural network structure is therefore implemented on a low cost FPGA device with high fault tolerance capability. The method is applied to a neural network based wind speed sensorless control of a small wind turbine system. The experimental results confirmed the validity of the developed Stochastic-ANN-FPGA implementation. The general implementation method can be extended to other power electronics applications with different feed-forward ANN structures.

Hybrid Stochastic and Neural Network Approach for Efficient FPGA Implementation of a Field-oriented Induction Motor Drive Controller

The FPGA (field programmable gate arrays) is concurrent, executing all its logic in parallel, therefore is good for neural network applications that are characterized as heavy parallel calculation algorithms. The stochastic arithmetic can simplify the computation elements and is compatible with modern VLSI design. This paper presents an efficiency approach for a single FPGA to implement the field-oriented control of induction motor drive based on stochastic theory and neural network algorithm. A stochastic neural network structure is proposed for a feedforward neural network to estimate the feedback signals in an induction motor drive. A new stochastic PI speed controller is developed with anti-windup function to improve the speed control performance. By applying the stochastic theory and neural network structure, the proposed algorithms enhance the arithmetic operations of the FPGA, save digital resources, simplify the algorithms, significantly reduce the cost and provide design flexibility and extra fault tolerance for the system. A hardware-in-the-loop test platform using real time digital simulator (RTDS) is built in the laboratory. The experimental results are provided to verify the proposed FPGA controller