Designing fuzzy type II PID controller for synchronous generator excitation

Author(s):  
Ahmad Kheirandish Gharehbagh ◽  
Mohammad Ali Labbaf Khaniki ◽  
Mohammad Manthouri
2012 ◽  
Vol 468-471 ◽  
pp. 309-313
Author(s):  
Juan Yu

To solve the problem that in excitation control system the parameters of proportional integral differential (PID) controller are difficult to be set, a summary on research results of current synchronous generator excitation control regulator parameters optimization. The article details the PSO algorithm of synchronous generator excitation control principles, and whose advantages and disadvantages. For the PSO algorithm converges slower early and late defects iteration, it introduces a new algorithm called adaptive chaotic particle swarm optimization (CAPSO), and using the chaotic tent mapping-based search methods to achieve improvements in partly-search, with a strong and robust search capabilities for the industry and scholars for reference.


2016 ◽  
Vol 136 (1) ◽  
pp. 18-24
Author(s):  
Daisuke Hiramatsu ◽  
Yoichi Uemura ◽  
Dai Nozaki ◽  
Shinji Mukoyama ◽  
Kazuma Tsujikawa ◽  
...  

2009 ◽  
Vol 18 (08) ◽  
pp. 1609-1625 ◽  
Author(s):  
MOHAMMAD KASHKI ◽  
YOUSSEF L. ABDEL-MAGID ◽  
MOHAMMAD A. ABIDO

In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CDCARLA technique and compares its performance with Particle Swarm Optimization (PSO) as an efficient evolutionary optimization method. The proposed method has been applied to PID controller design. The simulation results show the superior efficiency and robustness of the proposed method.


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