Neural Network-Based Stabilizer for the Improvement of Power System Dynamic Performance
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.
Author: Rudy Gianto, Kho Hie
Khwee
Journal Code: jptkomputergg170116
