Intelligent Optimal Mechanical Sensorless Control of Variable-Speed Wind Energy Systems Considering System Uncertainties

智能优化机械无传感器控制的变速风能系统考虑系统的不确定性（NSF提案ID：0901218）本研究的目的是开发智能优化机械无传感器控制的变速风能系统。 该方法是（1）开发基于神经网络的实时风速风向估计器和涡轮机轴速估计器，（2）开发基于自适应评价设计的风力涡轮机监控控制器，（3）利用进化群智能算法优化估计器和控制器的参数，（4）利用控制器对风力发电系统进行稳定性分析，及（5）以模拟及硬件实作验证建模及控制架构及方法。学术成就：本项目将深入研究利用生物启发的设计原则来处理高度随机、不确定及复杂的过程，例如：风能转换，以智能的方式。 通过建模、控制、稳定性分析、仿真和硬件实施，该项目将推进我们对生物启发建模、控制和优化技术的理解，以提高可负担、可靠和可持续的风电的可用性。该项目的成果将通过降低成本和提高效率、动态性能，风力发电系统的可靠性。 这些是实现供应全国20%的目标所必需的？这是风力发电。 将开发新的课程。 将支持本科生和至少一名女研究生从事该项目的各项任务。 通过与内布拉斯加州风能应用中心的合作？该项目将使农村K-12学校的教师和学生参与能源教育。

Intelligent Optimal Mechanical Sensorless Control of Variable-Speed Wind Energy Systems Considering System Uncertainties(NSF Proposal ID: 0901218)The objective of this research is to develop intelligent optimal mechanical sensorless control for variable-speed wind energy systems. The approach is to (1) develop a neural-network-based, real-time wind speed and direction estimator and turbine shaft speed estimator, (2) develop an adaptive critic design-based supervisory wind turbine controller, (3) optimize the parameters of the estimators and controller by evolutionary swarm intelligence algorithms, (4) perform a stability analysis for the wind energy systems with the controller, and (5) verify the modeling and control framework and methodology by simulations and hardware implementations.Intellectual Merit: This project will investigate in depth the use of bio-inspired design principles to handle highly stochastic, uncertain, and complex processes, e.g., wind energy conversion, in an intelligent way. Through modeling, control, stability analysis, simulation, and hardware implementation, this project will advance our understanding of bio-inspired modeling, control, and optimization technologies for improving the availability of affordable, reliable, and sustainable electricity from wind.Broader Impacts: This outcome of this project will further exploit the benefits of wind power by reducing the cost and improving the efficiency, dynamic performance, and reliability of wind energy systems. These are essential to reaching the goal of supplying 20% of the nation?s electricity by wind. New courses will be developed. Undergraduates and at least one female graduate student will be supported to work on various tasks of this project. Through the collaboration with the Nebraska Wind Applications Center?s Wind for Schools Program, this project will engage rural K-12 school teachers and students in energy education.