Power System Stability Enhancement Using Fuzzy Logic Based Power System Stabilizer

Author(s):  
V. K. Tayal ◽  
J. S. Lather ◽  
Piyush Sharma ◽  
S. K. Sinha
SINERGI ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 205
Author(s):  
Widi Aribowo

In this paper, a Distributed Time-Delay Neural Network (DTDNN) algorithm is used to control the Power System Stabilizer (PSS) parameters to find the reliable conditions. The proposed DTDNN algorithm apply tapped delay line memory to set the PSS. In this study, DTDNN consists of a DTDNN-identifier and a DTDNN-controller. The performance of the system with DTDNN-PSS controller is compared with a Recurrent Neural Network PSS (RNN-PSS) and Conventional PSS (C-PSS). The results show the effectiveness of DTDNN-PSS design, and superior robust performance for enhancement power system stability compared to other with different cases.


2015 ◽  
Vol 793 ◽  
pp. 139-143
Author(s):  
Muhamad Irwanto ◽  
N. Gomesh ◽  
Y.M. Irwan ◽  
F. Malek ◽  
M.R. Mamat ◽  
...  

Generally this project is to improve the dynamic power system stability using fuzzy logic power system stabilizer (FLPSS) which applied to the excitation system. This research is started by electric power system mathematic modelling (state variable equation) and block diagram then set membership function of fuzzy logic power system stabilizer (FLPSS). Block diagram model (plant system) is formed from state variable equation. The plant is controlled by fuzzy logic power system stabilizer (FLPSS) which its input and output from the rotor speed and to excitation system, respectively. To observe the oscillation of dynamic power system stability, the electrical power is varied ± 0.1 pu (positive and negative value indicate an increasing and decreasing electrical power, respectively). The result shows that using FLPSS, the oscillation of dynamic power system can be improved. The overshoot of electric power and rotor speed change oscillation after the disturbance is smaller than the conventional, and also the time to reach the steady state is faster.


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