Shuhui Li - US grants

The electrical engineering program at Texas A&M University- Kingsville (TAMUK) offers power electronics as an elective course and electric machinery as a required course. Due to increased perception that these courses are old-fashion and the materials covered in them are out-of-date, the credit hours of electric machinery courses have been reduced, and there is evident from recent years that shows a reduction in the number of students enrolling in these courses. However, the power industry demand for trained power engineers continues to grow! Hence, the objective of this project is to revitalize these courses with the introduction of start-of-art laboratories using digital control and digital signal processing (DSP). The goals of this restructuring include 1) to provide the requisite information about power electronics and electric drives in such a way by combining them with digital and DSP based control; 2) to make the reconstructed courses appealing and exciting; 3) to ensure the highest quality of education; and 4) to prepare students for industry as well as for advanced courses. As a consequence of these course revisions, a larger supply of trained power engineers is produced to meet modern power industry needs. The course restructuring and laboratory development are based on the materials and approaches developed at the University of Minnesota (UMN). The materials and approaches have been demonstrated to be successful at UMN through the significantly increased number of undergraduate students enrolled in the Power Electronic and Electric Drives courses and in their performance in these courses. This project is mainly targeting minority undergraduate students in South Texas area but working professional engineers in the chemical process industries and in the electric companies in this area can also benefit from taking these courses.

This project, acquiring a high-performance PC cluster, will enable computationally aggressive techniques to be applied in a variety of disciplines, with a focus on environmental disciplines that relate to the environment and economy of south Texas. This cluster is designed to support the following projects:
Integrated windmill and utility system simulation,
Modeling/simulation of linked and braided electroactive polymers,
Monte Carlo and molecular dynamics simulations of gas hydrates,
Modeling subsurface microbial competition,
Modeling coupled transport of colloids and contaminants, and their biological effects in river systems,
Decision support tools to estimate groundwater availability,
Modeling flow and transport of contaminants,
GIS-based flood and storm surge simulation and damage assessment,
Air quality forecasting,
Instrument and measurement research on estimate precision using ratio indicator, and
Using simulation-based genetic algorithms for dynamic signal control optimization in networks with stochastic route choice and time-variant demand.
These applications and the general computational methods to be employed have been planned. The system design includes a gigabit-ethernet-based interconnect and 128 processors with 2 GBytes of local memory, connected to a 1.2 TByte storage-area network. Management will include an Advisory Board and a Management Board. TAMUK is providing power/environmental support and system administrator personnel.

The objective of this research is to develop intelligent vector control technology, intelligent wind power extraction and management strategy, and advanced simulation and testing mechanisms by using Adaptive Dynamic Programming neural control mechanisms. This will improve the efficiency and reliability of wind energy conversion systems and enhance wind power integration into the electric utility system for the 20% wind penetration vision of the United States in 2030.

INTELLECTUAL MERITS
The intellectual merits of this research include development of: 1) Adaptive Dynamic Programming vector control technology to overcome the deficiency of conventional vector control technology, 2) intelligent wind power extraction and management techniques to improve wind power production efficiency and reliability, 3) intelligent control integration strategy under practical system constraints, 4) advanced simulation and testing mechanisms, and 5) a curriculum in Intelligent Sustainable Energy Systems to enhance student capability in multidisciplinary fields.

BROADER IMPACTS
The broader impacts of this research are significant. The rapid development and increased complexities of sustainable energy systems make it increasingly urgent to develop intelligent and cyber-enabled technologies for research and education of sustainable energy system field. This research will enhance optimal and intelligent technology for future smart and sustainable energy systems and increase the participation of an EPSCOR state in advanced scientific research. The research should have direct or indirect impacts to the following mission areas of the United States: reduction in imported energy, reduction of energy-related emissions, improvements in energy efficiency, and a technological lead for the United States in advanced energy techniques.

Today's electric power grid is experiencing a significant transformation because of rapid developments in renewable energy and smart grid technologies. This transition inevitably demands significant transformative research on many rapidly arising issues while the operation, stability, and reliability of the existing power grid is not affected.
This project supports the acquisition of a modern real-time hardware-in-the-loop (HIL) simulation system to advance smart grid and renewable energy technologies. The project will model smart grid components in a multi-disciplinary manner and investigate the interoperability characteristics of renewable energy sources, energy storage systems, and "smart" loads within a smart microgrid framework. The project will result in advanced computational technologies efficient for simulation-based study of both long- and short-term dynamics of future ?mixed system? and ?mixed signal? power grid configurations. At the same time, the project will examine how to integrate energy and intelligent-agent-based computing and communication systems within a decentralized electric power grid.
The project will enhance the development of advanced technologies for the future smart power grid, reduce energy-related emissions, improve energy efficiency of all economic sectors, and strengthen the technological lead for the United States in the advanced computing and energy fields. The project will attract and retain more minority and female students to the computational energy system program and educate young engineers and scientists to meet the rapid developments in energy and cyber technology for the 21st century.

This project establishes a multimedia-based virtual classroom with a virtual lab teaching assistant for the education of cyber physical system (CPS) security. Such a virtual classroom helps college students in resource-limited rural areas to learn the latest CPS security knowledge via an on-line peer-to-peer learning environment with other students from larger schools. This project includes three novel contributions: (1) all learning materials embrace an application-driven learning approach, with examples from diverse areas such as healthcare, renewable energy, and industrial controls used as the basis for CPS attack analysis; (2) with the help of a multimedia company, the project is building interesting virtual classroom lectures; and (3) to meet the open access lab requirements, the project is building interactive virtual lab helper software to enable remote students to conduct virtual hardware lab experiments and obtain help using multimedia tools. The design encourages innovative learning in several ways: developed labs require an iterative process with idea incubation to force students to follow a more mature creative design process; all labs intentionally include some ambiguity to encourage the search for multiple answers to a single problem; and the 3E (Explain-Exploit-Explore) based pedagogy is adopted for all CPS security labs/projects. The basic level labs emphasize concept explanations. The intermediate level senior projects require students to exploit previous knowledge to perform a multidisciplinary CPS security task. The advanced-level labs require independent exploration to reach creative solutions. The resulting teaching methodologies can be extended to other rural colleges and this project uses a proactive dissemination plan to achieve this aim.

This PFI: AIR Technology Translation project focuses on translating a neural network vector control technology to fill the need for renewable and smart grid control and integration. The technology is important because it will improve the power quality and facilitate an uninterrupted energy supply, increase incentives for consumers to use energy from renewable resources and electric vehicles, and accelerate progress towards America's target of deriving 20% of its electrical energy from renewable resources by 2030. The project will result in a prototype of a grid-connected power converter using the neural network vector control technology. This technology has the following unique features: fast response time, low overshoot and close to ideal control performance. These features provide the advantages of improved efficiency, reliability, stability and power quality of an electric utility system with integrated renewables as compared to the leading competing conventional standard vector control technology in this market space.

This project addresses the following technology gaps as it translates from research discovery toward commercial application: (1) proving the concept of the neural network control technology for grid-connected converters that meets the needs of electric power systems, (2) demonstrating a functional prototype power converter board that uses neural network control to integrate renewables into smart grids, (3) evaluating and benchmarking a commercially valuable solution of neural network control against conventional technology, and (4) developing a strategy for commercialization beyond this project. In addition, personnel involved in this project, PIs, undergraduates and graduates, will receive innovation translation experiences through activities from technical and business perspectives. The team of students, along with the PI, will participate in the Crimson Startup Canvas, UA's customer discovery program to understand the formal process through which to identify sustainable business models, increase the chance of attracting funding and investments, create new jobs and benefit society.

The project engages Southern Company Services to participate in an advisor role, offer guidance for the project, and guide commercialization aspects in this technology translation effort from research discovery toward commercial reality.

The broader impact/commercial potential of this I-Corps project is to act as a catalyst in the growth of distributed generation and microgrid industries. This artificial intelligence based control system will potentially provide an electrical network that is reliable by reducing outages and restoration costs with incredibly fast bidirectional power flow, secured with real time diagnostics, self-healing and adaptive capabilities, and more economical by reducing equipment failures and minimizing power losses. The product potentially three broad markets, including utilities, distributed generation and consumer. The solution will enhance energy generation from renewables, improve microgrid efficiency, reliability, stability and power quality, and add intelligent control to conventional power systems. Inverter capabilities are presently a significant challenge for integrating distributed generation sources. The proposed innovation would potentially provide an appropriate solution to address this challenge.

This I-Corps project develops a neural network control technology for microgrid control and management. Microgrids are one path for integrating renewable and distributed generation sources into the grid and can generally support a future smart electricity grid. A key challenge in microgrid adoption is adequate control of power inverters. Problems include high oscillations when connecting or disconnecting an energy source, fluctuating voltage and frequency, malfunctions and reliability, competing control between inverters, and high harmonic distortions. The proposed innovation uses adaptive dynamic programming and artificial neural networks to implement microgrid control. It integrates into one controller the advantages of conventional control methods, including optimal control, proportional integral control, predictive control, and sliding mode control. The proposed innovation has the potential to overcome the limitations of the conventional control technologies and better meet customer demands and requirements.