Title page for etd-0615115-141425


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URN etd-0615115-141425
Author Kai-Hung Lu
Author's Email Address No Public.
Statistics This thesis had been viewed 5567 times. Download 6 times.
Department Electrical Engineering
Year 2014
Semester 2
Degree Ph.D.
Type of Document
Language zh-TW.Big5 Chinese
Title Applications of Intelligent Controllers for FACTS to Transient Stability Study of Large-Scale Renewable Energy Integrated with Power System
Date of Defense 2015-07-09
Page Count 153
Keyword
  • Flexible AC Transmission Systems
  • Genetic Ant Colony Optimization
  • Functional Link based Elman Neural Network
  • Renewable energy system
  • Functional Link based Novel Recurrent Fuzzy Neural Network
  • Adaptive Intelligent Control System
  • Abstract The scale of nonlinearities and uncertainties of Renewable Energy System (RES) cause problems for dynamic control. This dissertation analyzes the dynamic and transient stability of the power system connected with a large RES including the analysis steady-state, dynamic and transient responses. An Adaptive Intelligent Control System (AICS) is proposed in the dissertation for a Static Synchronous Compensator (STATCOM) and a Unified Power Flow Controller (UPFC) to enhance the stability of the RES for power generation. An AICS can be used to increase the stability of the power control and improves the performance. Two models were proposed:
    1. Integration of Critic neural network (CNN), Functional Link based Novel Recurrent Fuzzy Neural Network (FLNRFNN) and Genetic Algorithm Hybrid Time Varying Particle Swarm Optimization (GAHTVPSO) algorithm.
    2. Integration of CNN, Functional Link based Elman Neural Network (FLENN) and genetic ant colony optimization algorithm (GACO).
    The node connecting weights of the FLNRFNN, FLENN and CNN are trained online. The learning rates of the FLNRFNN, FLENN and CNN are usually selected by trial and error method and are time-consuming. The GAHTVPSO and GACO approach were developed to adjust the learning rates of FLNRFNN, FLENN and CNN to improve the learning rate. The validity of these algorithms was demonstrated with many simulations. The simulation results show that AICS can achieve better damping characteristics as well as transient stability in Flexible Alternating Current Transmission Systems (FACTS) applications.
    Advisory Committee
  • Jyh-Yih Hsu - chair
  • Yu-Chi Wu - co-chair
  • Gary-W. Chang - co-chair
  • Jen-Hao Teng - co-chair
  • Wei-Min Lin - advisor
  • Files
  • etd-0615115-141425.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2015-07-15

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