Design dual input power system stabilizer for a multi-machine system based on focused- time- delay neural network
Corressponding author's email:
anhqh@hcmute.edu.vnKeywords:
Neural network, Power system stabilizerAbstract
In this paper, a novel technique for real-time tuning of the parameters of the dual input power system stabilizers (FTDNN-PSS) updated by error BP method in a multi machine system using Focused- Time- Delay Neural Network is presented. To simulate and evaluate the performance of Dual input FTDNN-PSS under wide variations in loading conditions such as three phase short-circuit on transmision line; light loading conditions; heavy loading conditions, the system response is compared with these cases where there is conventional power system stabilizer (PSS Kundur). Simulation results demonstrate the effectiveness and
robustness of dual input FTDNN-PSS in a multi-machine system, ensuring that generators quickly remain stable at a new position with better and faster damping and larger operating conditions.
Downloads: 0
References
Kundur -Two Area System”. Written by Jonas Persson, STRI AB, July, 1996 and revised September, 2004.
Jan Machowski, Janusz W. Bialek and James R. Bumby “POWER SYSTEM DYNAMICS Stability and Control” john wiley & sons, Ltd.
H.Demuth, mark beale, martin hagan, “Neural network Toolbox 5 User’s Guide”, Mathworks Inc.
Industrial Automation–Artificial Neural networks; Written by: Shady Gadoue; EEE 8005– Student Directed Learning (SDL).
Ravi Segala; A. Sharma, M.L. Kothari, “A self-tuning power system stabilizer based on artificial neural network” R. Segal et al. /Electrical Power and Energy Systems 26, pp. 423-430, 2004.
Lennart Ljung, System Identification ToolboxTM 7 User’s Guide, The MathWorks, 2011.
Downloads
Published
How to Cite
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.


