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.

Project Report

The doubly-fed induction generator (DFIG) wind turbine is a variable speed wind turbine widely used in the modern wind power industry. Presently, commercial DFIG wind turbines primarily use technology that was developed a decade ago. However, research is needed to enhance energy harvesting from the wind in more efficient and reliable ways due to the following reasons: 1) Recent studies indicated that an existing power converter control technology used in a DFIG wind turbine has a competing control deficiency. 2) The present design of DFIG wind power control system does not support the needs for optimal and smart control of the system. 3) Recent research on DFIG characteristics showed that optimal and artificial intelligence technology is favorable for efficient DFIG control and integration. Intellectual Merits: This project developed an optimal vector control technology based on adaptive dynamic programming (ADP) and artificial neural networks for a DFIG wind turbine, an advanced neural network training algorithm, and state-of-the-art simulation and hardware experiment facilities and mechanisms. This is the first-ever application of ADP to vector control of power electronic converters and induction generators. The specific outcomes and findings of the project include: 1) We have successfully completed the training and testing for both grid-side and rotor-side controllers for a DFIG wind turbine. 2) The neural network vector controller can track the reference d- and q-axis currents effectively even for testing trajectories and reference currents that are far away from the training data. 3) Compared to conventional vector control methods, the neural vector control approach produces the fastest response time, lowest overshoot, and, in general, the best performance. 4) In noisy, disturbance, and power converter switching environments, the neural network vector controller demonstrates strong capabilities in tracking the reference command while maintaining high power quality. 5) Under a fault in the grid system, the neural network controller for a DFIG wind turbine exhibits a strong short-circuit ride-through capability. 6) We integrated our neural network vector controller design with energy system principles and conventional control techniques. We found that this interdisciplinary neural network design approach can significantly enhance the performance and ensure the reliability and adaptive capability of neural network controllers under practical conditions and physical system constraints. 7) We built a hardware experiment system and conducted a series of tests to examine the performance of the neural network vector controllers in challenging hardware experiment conditions. Compared to conventional controllers, the neural network vector controllers demonstrated excellent performance with very fast tracking speed, reduced oscillations and harmonics, and improved stability and reliability. Beyond the proposed project scope, we conducted preliminary research in the following areas. i) Developed a new Forward Accumulation Through Time (FATT) algorithm and integrated FATT with Levenberg–Marquardt algorithm to improve training of recurrent neural networks. ii) Investigated microgrid control using neural networks. We found that the neural network vector controller has the potential to overcome many challenges for smooth transition and control of a microgrid between grid-tied mode and islanding mode. iii) Investigated how to apply neural network vector control technology to high voltage dc transmission systems. iv) Conducted preliminary research of nested-loop neural network controllers for ac electric machines and drives. v) Investigated control of grid connected inverters with LC and LCL filtering schemes for single-phase applications. vi) Investigated wind speed forecasting. vii) A video from the project website ( shows that in controlling an ac electric machine in generator or motor mode, the machine runs much more quietly when using our neural network controllers. Broader Impacts: The project has built a proper foundation on optimal and intelligent technology for future smart and sustainable energy systems. The technologies developed from the project overcome the competing control deficiency of standard vector control technology. Technically, this will result in optimal control and performance for grid integration of renewable resources and for the operation of electric power systems. For the power and energy industry, the technology generated from this research enhances energy generation from DFIG wind turbines and improves the efficiency, reliability, stability, and power quality of integrated wind and electric utility systems. For electric energy consumers, this research improves the power quality and uninterrupted energy supply to meet customers' needs and increases incentives for energy consumers to use less expensive, more reliable energy from wind resources and electric vehicles. For the United States, the project accelerates progress towards the energy development target of retrieving 20% of the nation’s energy from renewable resources by 2030 and helps the U.S. maintain its technological lead in the power and energy industry. The project is attracting, retaining and educating more minorities and women students to the computational and energy system program and educating young engineers and scientists to meet the rapid development of the energy and intelligent technology for the 21st century.

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University of Alabama Tuscaloosa
United States
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