A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses





fuzzy control, Genetic Algorithm, particle swarm optimization


With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.


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How to Cite

G. Silva, G. A. F. Souza, B. I. Fuly, and P. N. Vasconcelos, “A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses”, J. Appl. Methods Electron. Comput., vol. 11, no. 4, pp. 203–212, Dec. 2023.



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