Neural Network-Based Stabilizer for the Improvement of Power System Dynamic Performance

Rudy Gianto, Kho Hie Khwee

Abstract


This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs) to improve the oscillation damping and dynamic performance of interconnected multimachine power system. The scheme was based on the use of a neural network which identifies online the optimal controller parameters. The inputs to the neural network include the active- and reactive- power of the synchronous generators which represent the power loading on the system, and elements of the reduced nodal impedance matrix for representing the power system configuration. The outputs of the neural network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing system configuration and operating condition. For a representative power system, the neural network has been trained and tested for a wide range of credible operating conditions and contingencies. Both eigenvalue calculations and time-domain simulations were used in the testing and verification of the performance of the neural network-based stabilizer

Keywords


neural network, PSS, dynamic performance, oscillation damping, power system

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v15i3.3875

Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Universitas Ahmad Dahlan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


TELKOMNIKA Telecommunication, Computing, Electronics and Control
website: http://telkomnika.ee.uad.ac.id
online system: http://journal.uad.ac.id/index.php/TELKOMNIKA
Phone: +62 (274) 563515, 511830, 379418, 371120 ext: 3208
Fax    : +62 (274) 564604

View TELKOMNIKA Stats