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Integrated control of wind farms, FACTS devices and the power network using neural networks and adaptive critic designs.

机译：使用神经网络和自适应批评家设计对风电场，FACTS设备和电力网络进行集成控制。

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摘要

Worldwide concern about the environmental problems and a possible energy crisis has led to increasing interest in clean and renewable energy generation. Among various renewable energy sources, wind power is the most rapidly growing one. Therefore, how to provide efficient, reliable, and high-performance wind power generation and distribution has become an important and practical issue in the power industry.;In addition, because of the new constraints placed by the environmental and economical factors, the trend of power system planning and operation is toward maximum utilization of the existing infrastructure with tight system operating and stability margins. This trend, together with the increased penetration of renewable energy sources, will bring new challenges to power system operation, control, stability and reliability which require innovative solutions. Flexible ac transmission system (FACTS) devices, through their fast, flexible, and effective control capability, provide one possible solution to these challenges.;To fully utilize the capability of individual power system components, e.g., wind turbine generators (WTGs) and FACTS devices, their control systems must be suitably designed with high reliability. Moreover, in order to optimize local as well as system-wide performance and stability of the power system, real-time local and wide-area coordinated control is becoming an important issue.;Power systems containing conventional synchronous generators, WTGs, and FACTS devices are large-scale, nonlinear, nonstationary, stochastic and complex systems distributed over large geographic areas. Traditional mathematical tools and system control techniques have limitations to control such complex systems to achieve an optimal performance. Intelligent and bio-inspired techniques, such as swarm intelligence, neural networks, and adaptive critic designs, are emerging as promising alternative technologies for power system control and performance optimization.;This work focuses on the development of advanced optimization and intelligent control algorithms to improve the stability, reliability and dynamic performance of WTGs, FACTS devices, and the associated power networks. The proposed optimization and control algorithms are validated by simulation studies in PSCAD/EMTDC, experimental studies, or real-time implementations using Real Time Digital Simulation (RTDS) and TMS320C6701 Digital Signal Processor (DSP) Platform. Results show that they significantly improve electrical energy security, reliability and sustainability.
机译：世界范围内对环境问题和可能的能源危机的关注导致对清洁和可再生能源发电的兴趣日益增加。在各种可再生能源中，风能是发展最快的一种。因此，如何提供高效，可靠，高性能的风力发电和配电已成为电力行业的重要和现实问题。电力系统的规划和运营将以现有的基础设施实现最大的利用，并获得严格的系统运营和稳定的利润。这种趋势，加上可再生能源的日益普及，将给电力系统的运行，控制，稳定性和可靠性带来新的挑战，这需要创新的解决方案。灵活的交流输电系统（FACTS）设备通过其快速，灵活和有效的控制能力，为应对这些挑战提供了一种可能的解决方案。充分利用各个电力系统组件的能力，例如风力发电机（WTG）和FACTS设备，其控制系统必须经过适当设计，具有很高的可靠性。此外，为了优化电力系统的局部及系统范围的性能和稳定性，实时局部和广域协调控制已成为一个重要问题。包含常规同步发电机，WTG和FACTS设备的电力系统是分布在较大地理区域上的大规模，非线性，非平稳，随机和复杂系统。传统的数学工具和系统控制技术在控制此类复杂系统以获得最佳性能方面存在局限性。群智能，神经网络和自适应批评家设计等智能和受生物启发的技术正在成为电力系统控制和性能优化的有前途的替代技术。；该工作着重于开发高级优化和智能控制算法以改进WTG，FACTS设备以及相关电力网络的稳定性，可靠性和动态性能。所提出的优化和控制算法已通过PSCAD / EMTDC中的仿真研究，实验研究或使用实时数字仿真（RTDS）和TMS320C6701数字信号处理器（DSP）平台的实时实现进行了验证。结果表明，它们显着提高了电能安全性，可靠性和可持续性。

著录项

  • 作者

    Qiao, Wei.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Alternative Energy.;Engineering Electronics and Electrical.;Energy.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 335 p.
  • 总页数 335
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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